{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Compare ensemble classifiers using resampling\n",
    "\n",
    "Ensemble classifiers have shown to improve classification performance compare\n",
    "to single learner. However, they will be affected by class imbalance. This\n",
    "example shows the benefit of balancing the training set before to learn\n",
    "learners. We are making the comparison with non-balanced ensemble methods.\n",
    "\n",
    "We make a comparison using the balanced accuracy and geometric mean which are\n",
    "metrics widely used in the literature to evaluate models learned on imbalanced\n",
    "set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>\n",
    "# License: MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load an imbalanced dataset\n",
    "\n",
    "We will load the UCI SatImage dataset which has an imbalanced ratio of 9.3:1\n",
    "(number of majority sample for a minority sample). The data are then split\n",
    "into training and testing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.datasets import fetch_datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "satimage = fetch_datasets()[\"satimage\"]\n",
    "X, y = satimage.data, satimage.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification using a single decision tree\n",
    "\n",
    "We train a decision tree classifier which will be used as a baseline for the\n",
    "rest of this example.\n",
    "\n",
    "The results are reported in terms of balanced accuracy and geometric mean\n",
    "which are metrics widely used in the literature to validate model trained on\n",
    "imbalanced set.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, y_train)\n",
    "y_pred_tree = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree classifier performance:\n",
      "Balanced accuracy: 0.73 - Geometric mean 0.70\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from imblearn.metrics import geometric_mean_score\n",
    "\n",
    "print(\"Decision tree classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_tree):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_tree):.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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MxnmoiYxG/WYMuv4zMTERx4wZI+7Zs0evbZWXl4tvv/22aG1trdN2zMzMxFmzZmmtc+/evaKvr69O9b311ls11q/vZgybNm0SBUHQqX6FQiHOnDmzzrsl6Xs/YDc3t3q3+dRTT+l9P+D6qO8Ttd1RSF/Z2dlily5dtL4Pdbreo7k2jbGPnT17VgwKCtL5/0e7du3EM2fO6PkpkT54BEzNnrm5Oezt7eHg4AB3d3f07NkTQUFBGDp0KLy8vPSuz8TEBO+99x5mz56NtWvXYs+ePbh48SLS09NRVlYGe3t7+Pj44IEHHsDgwYMxatSoemdCCgsLw+XLl7F+/Xps374d0dHRSE1NRUlJCRwcHNChQweEhoZi3LhxGDBggN5tfvLJJ6WjqqNHjyImJgY3btxAdnY2gIqZpTp16oT+/ftjypQpRpuXesyYMbh27Rp++OEHbN++HRcuXEB6ejqsrKzg6emJwYMHY8qUKRqjde9XDg4OOHXqFFauXIk//vgDcXFxyM7ObpTzwY2xj3Xv3h3R0dHYtWsXfvvtN+mewtnZ2bCwsICrqys6d+6M4OBgjBgxAqGhoffdaPTmRhBFtYk/iYiI6J7gICwiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGfAypPtEYmIitm/fLi23b9/+ntwjloiIjCM/P19jxrcxY8bA29u7zvIM4PvE9u3bMWvWLLmbQURERjRz5sw6X2MXNBERkQwYwERERDJgF/R9on379hrLyz9yQWCA9hsDEDVFrzzWUe4mEDWKPGTjMs5Ky9W/16tjAN8nqg+4CgywQP9gK5laQ9R4WgkucjeBqHFUm9i5voG07IImIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSgancDaCWQ6kEbl62xJWz1rgSa40rZ60RH2eJ8rKK34GBofn4/NdrOtVVUiTg0hkbxP1tjfg4K9y+boG0JDMU5psAImBtp4Rnu1L49y7AkMez0OGBonrrfO2JDoiNsm3Qe3PzKsXakxd1KqtSAdH77XDkj1a4dMYamalmKClSwNGlHE5uZfAPKkD3vvno0T8fltaqBrWHmr6K/w/XG7z+F/PaYvdGp3rLuXqWYuAj2Qgelgt371I4upSjqFCBrFQz3L5hgdgoG5w+aIdb1ywb3BaqHQOY7oljOx3wyUveKCkyMUp9WyNd8P37bep8PSdDgZwMM8T9bYPN/22Nh8Zm4aWPb8PeUWmU7Vfn2LpMp3LXz1thyeteuHLWpsZrd2+b4+5t84o2f9saC76Nx4AxOcZuKrUQWanav95NzVSYMCsNk2bfhYWVqPGauaUSDk5KtOtSjP6jcpBwyRL/DuvcmM1tkRjAdE/k55oYLXyrs7BSwrtjCTx8SmDroISyXEB6shniTtugMK9imwe3OiLxqiW+/O0qbOxqP6rsOzIbPp3rP1IGgIJcE+zbXHV0EfZ4Vr3r/H3QFoumtUdpccURv8JERKfuhfDwKYGVrQr52Sa4fd0SCZctoVIKOrWDmq/0FDNsXe2sc/leA/Pg5VcKAMhMNcXpw3Z1ljUzV+Ht7xMQPDRPei4z1RSXz1gjK80UpmYinN3L4Ne1GK1cyhv+JkgrBjDdU46uZejUvRCdehSic49CRB+wx2/fu+pdTxvfEoT/Jwm9B+WhfUARTGrZk0uLBWz5zhWRn3pApRIQH2eF1R974KWP7tRa57jn03Xe/tbVLlIAm5mrMPgx7QF8+ayVRviOmZqOya+k1Prllp9jgqi/7OHWtlTn9lDzkxRvga/f8tKprEIh4qe/q06B7N/sqPVH3BsrEqXwTUowx6qFnjix2x5A9XVEdHigCA8EF+jbfNIBA5juid6Dc/HjyQto7aXZVXvpdM2uWF30fTgXfR/O1VrG3FLExNmpKCsT8OMXHgCAvb84YcY7STC3FLWuW5/dmxylx32G5MLeqe6u7bJSAV/+n7cUvjM/uI1Hp9cd9rYOSgybUP8RNVGloEF5cHar+jGnvn9WN3hclnRq4+YVC7z2hB9yMszqKC3g2jlrXDtnbczm0j9a7ChopVKJ8+fPIzIyErNnz0ZoaCisra0hCAIEQUB4eLjcTWxWnFqX1wjfe2XEpEzpcWG+CZISLAyqL/GqhcY53GETMrWUBravdcbNy1YAgNAROVrDl6gh1PfBq+esEB9nVWs5UzMVZixMAlAxKPKz2d5awpcaW4s9Ap4wYQI2b94sdzPoHnBw1uzmLcw37Hen+shSB+cyPBim/Uj8j7Uu0uNJs+8atG2i6mzslQgZVrUP7tlY99Fvv1E5cGpd8f/h9EE7HtnKrEUfAatzcnJCx44dZWoNNabEK5qXTxhyblWlAvZtrvqCGzwuG6ZaDiCuxFhJl2+4eJSiS6/CBm+bqDYDH8mWRjGXlQrYv6XuAB6iNljwyA6HRm8baddij4D79OkDf39/BAUFISgoCL6+voiMjMS0adPkbhoZUVmpgB8+8pCWA3rna5wr09eZw7ZITzaXlofX0/184VRVV3Xltci5mSbYsc4Zh7e3QkqiOcpKBTi6lsM/qACDHs1GyHDtR9RE6tS7n0/ts0NOZt1f6wG9q34AXo2t6KbuOSAPI5/JQJdehXB0LUdhvgLJCRaIPmCH7WuckZ3OLurG0mIDeP78+XI3gRpJWamAzFRTnD9hi19XueL6hYpuNmtbJWZ+UPsIaF3t2VTV/ewbUAS/btovW7pytqqLr3WbUsQcs8Uns3yQeVfzSy0l0QQpiRbYv8UJgaH5eOvbeLRybpxrlqn58PQtQdcHq0JV28Qbnu1KYKd2HXxWqhle+SoRwydqDvgzt1CilXMh/IMK8eQLaVj5tid2rdf9cijSXYsNYGpeRrbtrvWyC6/2xXj7+wS061Lc4G0U5itwdGdVt92w8dqPfgEgLanqaDnlljkWTvVFUYEJFAoR/kEF8PIrQWmJgAsnbZF6p6JsbJQtXnm0I5btuAIbe86ERXUb+mTVPpiTaYKTe+u+9tfVU3MQ5L/fTcKgR7MBAFlppjgXZYOCPBO4e5eiW3ABzMxFWNmo8Mri2zA3F7FdbSwDGQcDmJo1hYmICbPuYsqrKbVeK6yPw9tbSZOJmJiKGPJE/ZcK5edWTT5yck9FeLdpX4y3/psAv65VPwZUKmDrDy7476I2UKkE3L5hia8XeOH1ZYmGNZqaMRFDnsiWlvZvcZSmda2Njb1mj0pl+P5vSWus+8pNY1137xLMX3UTnXtU9PD8e1ESYo7ZcjpKI2uxg7CoeRkbno5HwtPwSHgahk3IQI/+ebC2VUKlFLB+mTuef8gfp/bXfXSgC/Xu596DcnWaIai4UPO/mLWtEh+vv64RvgCgUACPPZ+OaW8mS8/t3+yIO/HmIKpNYGgB3L2rBhTu1jL6GUCt84pv+d4Faz7zqBHcKYkWmP9Ue6QnV/xqNbcUMfGlVCO0mtQxgKlZePH9O3jpo4p/ry65hU83XsfPZy/gubeSYGahQlK8Bd6Z3L7eL6m6pNwyx7njul/7W8ncQvNL75Fp6XDTcj30E/9OhZNbxesqlYAj21vp31hqEdT3wfg4y3ovKSot0TxFU1wk4Mcv3Ossn59jivXL3aTl/qNzoDAxbAIb0sQuaCNKTExEYmLDugxjY2ON3BqytFZhwqxUtGlfgvee84VKJWDZf9qiW3ABPHz0uxRp7y+OEMWKLzA7x3KdRypb2WgGcL+R2VrLm5gCwUNzsHNdxfm2i9ENmymMmjcLKxX6jaq6UcceLTNfVSou0Dzeijlqi4Jc7fOzH93pIE3damWjQvuAIl47bEQMYCP64Ycf8O6778rdDKqm38gc9Oifh7NH7FBarMC2SBdpNiBd7fmlqvt50NgsmJnrdiRg76jZTe3TqaTedbw7VpVJT+ElIFRTv5E50k1FlOWa16bXJTdL8+s+8Wr953Mz75qhIFchDQZ0di/DtXMNaDDVil3Q1CL0Glh11xd9jyovnLRBUnzV9JVD9Zin2atDVZgKgqjT/X2tbKoGyxQV8L8o1aTe/fz3QTtkptb/Q+3WNc0pWHXdtwrzq46SrW05Kt+Yms0R8KVLl3Dp0qU6X+/SpQu6dOlyD1tE9xO7VlWhlpul320R1Se29+5YjC49dZ/Nql3nqsFWoiiguFBRbwgXFVS1r65bJ1LL5eJRiu798qVlbdf+qisqMMHd22bSGITqp0fqYm1b9X+nvi5r0k+zCeD169dr7f5duHAhFi1a1KhtmD59OoYOHdqgdWNjYzFr1iwjt4gqZd6t2tXVw7g+pcUCDqsNhBqqw7W/6nr0z9NYvnnFQrq0oy6JV6uOVFw9eUtC0hT2RBZM/snBvGwTRO2y13nds0dsMWJSRQ+Od8f6r4l3civTuBY9LYmnRIyp2QTw/cDb2xve3t5yN4NqcWJP1QQabTvoPhnHsb8ckJ9T8d9EodDt2l91bl5l6BhYiKuxFQNXjv3poDWAleXAyb1VX6gPhObXWZZapmHjq/bBQ1tboaxE99MUR3e0kgK4e98CWNspUZhX91Ft34erBnrlZpog4RKvAzamZnOCadGiRRBFsc5/jX30S/dOcaECpcV1z3pV3bY1zrgSUzVys//oHC2lNalf+9tzYB5cPPS/peJjz6dJj7eudkXqnbqPIrZ874qMlIprf80tVXhobLbe26Pmq3OPQo1Berv0vKzu1H473Lpe0cNiaa3Csy+n1FnW2k6JSbOrrv3do3YlABlHswlgajnu3LDAtH7+2LRSe5hlpppi1UJPfD3fS3quW3C+xq3btMlMNcXfB6sm79Bl6snahD2ehQ7dKs4bF+aZ4M2JfoiP0zySEEVgW6QzIj70lJ577Lk06dZxRAAwVG3w1a3rFrh0Wr8BhSqlgIgPq25O8sS/0zH51RSYmGqO6nfzKsVH/7shTV+Zl2WCTd+0NqDlVBt2QdM9s+DZ9siodllNVlrVLnglxgovDu1cY70PfroOZ3fNIEpPNsf3H7TB9x+0gVvbErTrXAx7p3KYWYgozDPB7WsWuBFnpTE/tJdfMeavStC5vfs2O0rr29grNbrj9KFQAG9/n4B5j3REVpoZbt+wxMxhneHfuwBt1eaCvnu7atarwL55CH8jWUut1NKYmqkwSK1HRNt9f7WJ+tMBG1e6YsLMip6ZZ1++i1HPZiA2yhaFeQppLmhzi6pbHH7ykneNG4iQ4RjAdM8kXrHUCJnqigtNcOOiVY3ny6pNk2diJkKhEKFSVYTj3VsWuHvLosZ6lRQKESOeysBzbyXrNQBLvft54CNZ0j1XG8LduxSf/XINn832wdVYa6hUFaF74aRtjbLDJmRgzqe3DZ67mpqX4KG5sHeq2H+Vyoou4YaK+MADBTkmeObluzC3EOHUulyaG1pderIpPpvtg5hjNfdTMlyL/S8eHx+PiIgIjefUZ6M6c+YMFixYoPF6WFgYwsLC7kn7qG7tOhfj55jzOH3QDhejbRAfZ4WURHPkZpmivEyAta0Sdo5K+HYpQsCDBRj8WLbe526vnbNCfFzVjwH1gS8N5d2xBEu3X8Ghba1w4HdHxMdZIivNDKZmIlzcy9C9bx5GPJWJjoHaR0lTyzRM7frzmKOa96XWn4D1y91waHsrjJiYid5huXD1LIOVjQp5WSaIj7PC8d32+Gu9E0qLeaaysQiiKLbIyT0PHDiAwYMH67VOY17KdOTIEQwYMEBaPvhbG/QPrnk0SNTUjfDsIXcTiBpFtpiOaByQlg8fPoz+/fvXWZ4/bYiIiGTQYrugBw0ahBZ68E9ERPcBHgETERHJgAFMREQkAwYwERGRDHQ6B2xi0vh3wBAEAeXlnPWHiIhaBp0CWBRFCILAQUtERERGonMXNMOXiIjIeHQ6AlapeFNwIiIiY+IgLCIiIhkwgImIiGTAACYiIpJBo05FWVRUhKysLJSXl8Pb27sxN0VERNSkGDWARVHEL7/8gnXr1uHIkSPIyqq4fVZt1/imp6dj48aNAICOHTti2LBhxmwKERHRfc1oAXz58mVMnDgR586dA1D/ZUvOzs5YsmQJrl+/Dnd3d9y6dQsKBXvEiYioZTBK4sXFxSE0NBTnzp2DKIoQRRE2NjawsbGpcx1BEPDiiy9CFEWkpKTgwIEDxmgKERFRk2BwACuVSjz++OPIzs6GKIoICwtDVFQU8vLyEB4ernXdCRMmSI937dplaFOIiIiaDIO7oNetW4fLly9DEASMGzcOGzdu1LkruU2bNvD19UVCQgKio6MNbQoREVGTYfAR8JYtWwAAlpaW+Oabb/Q+j9utWzeIooirV68a2hQiIqImw+AA/vvvvyEIAvr37w9XV1e913dxcQEAZGRkGNoUIiKiJsPgAE5LSwMAtGvXrkHrm5mZAQBvRUhERC2KwQFsYWEBACgrK2vQ+pUB7ujoaGhTiIiImgyDA9jNzQ0AGnwO9+TJkxAEAW3btjW0KURERE2GwQEcGhoKURRx6tQp3L17V691d+/ejTt37gAABg4caGhTiIiImgyDA/ixxx4DUNEF/eabb+q8Xl5eHubMmSMtjx8/3tCmEBERNRlGCeDu3btDFEWsWbMGL7/8MkpLS7Wuc+7cOQwcOFC6fnjIkCEIDg42tClERERNhlHmgl6zZg0GDhyIvLw8LF26FBs2bMDjjz+OmJgYqczSpUuRkpKCo0eP4tixY9Jc0S4uLoiIiDBGM4iIiJoMowRwYGAgtm7digkTJiA1NRUpKSlYuXIlgIo5nwHg5ZdflspXhq+Hhwd+//13DsAiIqIWx2i3Hxo4cCBiYmIwbdo0mJmZSTdlqO2fqakpwsPDER0djd69exurCURERE2GUe8H7ObmhoiICHz66afYs2cPoqKikJSUhJycHNjY2MDNzQ3BwcEYMWIEPD09jblpIiKiJsWoAVzJxcUFkyZNwqRJkxqjeiIioibPaF3QREREpDsGMBERkQwapQsaAK5cuYIzZ84gLS0NeXl5sLOzg4uLC3r16oVOnTo11maJiIiaBKMGcE5ODr766itEREQgKSmpznIeHh54/vnnMW/ePLRq1cqYTSAiImoSjNYFvXv3bgQEBOD999/HnTt3tF6GlJSUhPfffx8BAQH466+/jNUEIiKiJsMoR8Dbtm3D+PHjUVZWJk2yYWJigs6dO8Pb2xs2NjYoKCjArVu3cOnSJSiVSgBASkoKxo4di02bNmHs2LHGaAoREVGTYHAA3717F+Hh4dL8zy4uLnjnnXfw7LPP1tq9nJOTg59++gnvv/8+0tLSUFZWhmnTpuHixYvSrQ2JiIiaO4O7oFesWIGsrCwIgoBu3brh3LlzeOmll+o8t+vg4IBZs2YhJiYGXbt2BQBkZ2djxYoVhjaFiIioyTA4gLdu3VpRkUKBjRs36nwU6+bmhg0bNkChUGjUQ0RE1BIYHMAJCQkQBAH9+vVDly5d9FrX398f/fv3hyiKSEhIMLQpRERETYbRRkE39NpeXhNMREQtkcEB7OXlBQDIz89v0PqV61XWQ0RE1BIYHMDDhw+HKIo4cuSI3utWricIAoYOHWpoU4iIiJoMgwP4hRdegIWFBe7cuYOlS5fqte6KFStw+/ZtmJub44UXXjC0KURERE2GwQHcuXNnLFu2DKIo4tVXX8UXX3wBlUqldR1RFLF48WL83//9HwBgyZIl8Pf3N7QpRERETYZOE3EkJiZqfX3EiBFYsmQJXn/9dbzxxhtYtWoVnn76aYSGhsLb2xvW1tYoLCxEYmIijh8/jp9//hnXr1+Hubk5PvvsM4wcORKJiYnw9vY2ypsiIiK63+kUwO3atYMgCDpVKIoibty4gQ8//FBrGQAoLS3FvHnzMG/ePAiCgPLycp22QURE1NTpNRVlZXDWRRAEKai1lVUP8/rqJCIiao50CmBvb2+dj4CJiIiofjoFMGepIiIiMi6jzYRFREREumMAExERyYABTEREJAMGMBERkQz0ugxJXzk5OcjLy6t3ZqxKnIiDiIhaCqMG8M2bN7Fq1Srs2bMH586dQ1lZmc7rciIOIiJqSYwWwF988QUWLFgghS4n2CAiIqqbUQL4888/xxtvvCEt29raQhAE5OXlQRAEeHt7Iy8vD1lZWVIwC4IAS0tLtG7d2hhNICIialIMHoR169YtLFiwAEBF8G7YsAHZ2dmYMmWKVCY+Ph7p6enIzs7GH3/8gdGjR0MURZSVleHf//434uPjER8fb2hTiIiImgyDA/i///0vysrKIAgCVqxYgfHjx0OhqL1aOzs7jBw5Etu2bcPPP/8MQRDw1ltv4b333jO0GURERE2KwQG8f/9+AICLiwsmT56s83oTJ07E4sWLIYoi3n//fcTExBjaFCIioibD4AC+fv06BEFAcHBwnTdsqGt088yZM+Hh4QGVSoUffvjB0KYQERE1GQYHcFZWFgDAw8ND43kLCwvpcWFhYa3rCoKAAQMGQBRF7Nu3z9CmEBERNRkGB7C5uTkA1Dj6tbe3lx7fvn27zvVtbW0BAHfu3DG0KURERE2GwQFceRlRTk6OxvPt2rWTHp8+fbrO9W/cuAEAKCoqMrQpRERETYbBARwQEABRFHHt2jWN53v27Ck9/vnnn2td98qVKzh69CgEQYCnp6ehTSEiImoyDA7gfv36AQAuXLiAkpIS6fkHHngAnTp1giiK+PPPP/Hhhx9CqVRKryckJODpp5+WZs4aPHiwoU0hIiJqMgwO4OHDhwMASkpKcODAAY3X3nzzTenxO++8g9atW6Nfv37o2bMnOnbsiDNnzgAATE1N8X//93+GNoWIiKjJMDiAe/Xqhd69e6N169bYtm2bxmtTp05FeHg4RFGEKIrIysrC8ePHERsbC6VSCVEUoVAosHz5cnTt2tXQphARETUZRpkL+uTJk3W+9sMPPyAkJARffvklrl69qjEXdEhICN5//32EhYUZoxlERERNRqPeD7jSjBkzMGPGDNy+fRtJSUlQKBTw9fWFs7Pzvdg8ERHRfeeeBHAlLy8veHl53ctNEhER3ZcMPgdMRERE+mMAExERyYABTEREJAOdzgFPnz69sdsBQRAQERHR6NshIiK6H+gUwJGRkXXeatCYGMBERNRS6DwKuvL63cZyLwKeiIjofqFTAK9evbqx20FERNSi6BTAU6dObex2EBERtSgcBU1ERCSDezoTFunulcc6opXgIncziIyP4z2o2RIAPYZL8QiYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhk0KhzQRcVFSErKwvl5eXw9vZuzE0RERE1KUYNYFEU8csvv2DdunU4cuQIsrKyAACCIKC8vFyjbHp6OjZu3AgA6NixI4YNG2bMphAREd3XjBbAly9fxsSJE3Hu3DkAFWGsjbOzM5YsWYLr16/D3d0dt27dgkLBHnEiImoZjJJ4cXFxCA0Nxblz5yCKIkRRhI2NDWxsbOpcRxAEvPjiixBFESkpKThw4IAxmkJERNQkGBzASqUSjz/+OLKzsyGKIsLCwhAVFYW8vDyEh4drXXfChAnS4127dhnaFCIioibD4C7odevW4fLlyxAEAePGjcPGjRt17kpu06YNfH19kZCQgOjoaEObQkRE1GQYfAS8ZcsWAIClpSW++eYbvc/jduvWDaIo4urVq4Y2hYiIqMkwOID//vtvCIKA/v37w9XVVe/1XVxcAAAZGRmGNoWIiKjJMDiA09LSAADt2rVr0PpmZmYAUOMyJSIioubM4AC2sLAAAJSVlTVo/coAd3R0NLQpRERETYbBAezm5gYADT6He/LkSQiCgLZt2xraFCIioibD4AAODQ2FKIo4deoU7t69q9e6u3fvxp07dwAAAwcONLQpRERETYbBAfzYY48BqOiCfvPNN3VeLy8vD3PmzJGWx48fb2hTiIiImgyjBHD37t0hiiLWrFmDl19+GaWlpVrXOXfuHAYOHChdPzxkyBAEBwcb2hQiIqImwyhzQa9ZswYDBw5EXl4eli5dig0bNuDxxx9HTEyMVGbp0qVISUnB0aNHcezYMWmuaBcXF0RERBijGURERE2GUQI4MDAQW7duxYQJE5CamoqUlBSsXLkSQMWczwDw8ssvS+Urw9fDwwO///47B2AREVGLY7TbDw0cOBAxMTGYNm0azMzMpJsy1PbP1NQU4eHhiI6ORu/evY3VBCIioibDqPcDdnNzQ0REBD799FPs2bMHUVFRSEpKQk5ODmxsbODm5obg4GCMGDECnp6extw0ERFRk2LUAK7k4uKCSZMmYdKkSY1RPRERUZNntC5oIiIi0h0DmIiISAYMYCIiIhkYfA547dq1xmgHAGDKlClGq4uIiOh+ZnAAh4eHS9f6GkIQBAYwERG1GEYZBV05sYauBEHQex0iIqLmxOAAnjp1qk7lVCoVcnJycO7cOcTHxwMALC0tMX78eCgUPBVNREQti8EBvHr1ar3XiY6Oxty5cxEVFYWUlBRs2rQJ9vb2hjaFiIioyZDl0LN37944dOgQhg8fjj179vDcLxERtTiy9f2amJjg+++/h5mZGbZt24bNmzfL1RQiIqJ7TtaTr15eXujXrx9EUWxQVzYREVFTJfvoJz8/PwDQuHcwERFRcyd7ABcXFwMAUlNTZW4JERHRvSNrAKtUKhw6dAgA4ODgIGdTiIiI7ilZA3jBggVITEyEIAjo1auXnE0hIiK6pwy+DjgxMVHnsuXl5cjIyMDZs2exZs0aREVFSa/pOqEHERFRc2BwALdr187guaBHjRqFSZMmGdoUIiKiJsNoXdCiKOr9TxAEzJw5E7/88ouxmkFERNQkGHwE7O3trfMRsJmZGezt7dGuXTsEBwdj4sSJ8Pb2NrQJRERETY7BAZyQkGCEZhAREbUssl8HTERE1BIZfAQcGxsrPe7atStMTEwMrZKIiKjZMziAe/ToAUEQ4OPjgxs3bhijTURERM2ewV3QZmZmAICQkBCDG0NERNRSGBzA7u7uAABbW1uDG0NERNRSGBzAXbp0gSiKuHnzpjHaQ0RE1CIYHMATJkwAABw5cgQZGRkGN4iIiKglMDiAn3nmGQQEBKC4uBizZs0yRpuIiIiaPYMD2NLSEr/88gvatm2LTZs2YdSoUbhy5Yox2kZERNRs6XwZ0nvvvQcA6NOnDx5++OEaz48dOxarVq3CX3/9BX9/fwQGBiIoKAiurq6wsrLSaRvvvPOOPm0nIiJqsgRRFEVdCioUCgiCgFmzZmHZsmU1nldXeaMFfSmVSr3XaS6OHDmCAQMGSMu9MQitBBcZW0TUSAy8exrR/SpbTEe0uF9aPnz4MPr3719neYMn4gAqAleX57Qx9JaGRERETYnBAbxw4UJjtIOIiKhFYQATERHJgHdDIiIikgEDmIiISAYMYCIiIhkwgImIiGSg9yCsLVu24Pz580ZviCAI2Lt3r9HrJSIiuh/pHcBJSUlISkoyaiMaOnEHERFRU6V3AOs7wQYRERHVpHcA9+nTByNHjmyMthAREbUYDQpgTr5BRERkGI6CJiIikgEDmIiISAYMYCIiIhkY5XaERPdaYGg+Pv/1eoPX/2JeW+ze6KTxnJtXKdaejNOrnqR4c0zr59/gdhDpyj+oAEOeyESXXoVw9yqFla0SJcUKZKeb4vp5axz70wFHdjigrFT7cZWjaxl6DcxDYGg+2gcUwd27FNa2ShQXKpCVZoZLZ6xx+I9WOLnHHqLIy0MbEwOYWqSsVO761DTYOZbj5S8S0ffh3BqvmZqpYGNXija+pRj4SDaS4s3x+TxvXIy2rVHW1bMUry5JxAMh+TAxqbkdWwcVbB1K0LZDCYaNz8L1C5b4bI4PEi5ZNcbbIjCAqYlKTzHD1tXOOpfvNTAPXn6lAIDMVFOcPmyntXxBngJ7f3Gst97sDP4XosZjbqnCJ+uvo0O3Ium57HRTXDtvhfRkMzg4l8OnUzE821Xs256+pfj45xt4fYIfLp+x0ajL2b0MPfrlazyXcssMNy5YITvdDJbWKnTqUQiv9iUAAL+uxVj821X8Z2IHXImxbuR32jLp9e3BSTjofpEUb4Gv3/LSqaxCIeKnvy9Ky/s3O0Kl1N61lpdtonP9RI1lwsy7UviqVMDaz93x67etUVqs3s0s4qGx2ZjzyS3YOqhgaa3CvM9u4cVhXWqtMz3ZDH9tcMKeTU5ISrCo8Xro8BzM+yIRrZyVsLFT4a3/xuNfg/yrbZOMQecAjo+PBwDY29s3WmOIGkPQoDw4u5VLy7s31X9kS3Q/GDYhU3r8+w8u+HmZey2lBBzc6gilUsDb3yYAANoHFKNdlyKN7uPCPBOsWuSJ7WtdUFZSd5hG7XJAxmQ/LNl6BSamgHvbMgx9MhM7fnIx1tuif+j8k8bHxwc+Pj5wdGw+X15KpRLnz59HZGQkZs+ejdDQUFhbW0MQBAiCgPDwcLmbSEag/iV29ZwV4uN4Tovuf9a2Sri3LZOW9/+m/bv32J8OKC6s+kqv7EqulHjVElu+a601fCtdibHGkR2tpOU+Q2qefybDtegTWBMmTMDmzZvlbgY1Iht7JUKGVX157NnYfH5AUvNmaaPSWM7PqWXklBqVUkBBngKW1hXrCQb2GF84ZYOHxmYDqLhCgIyvRXfqK5VKjWUnJyd07NhRptZQYxj4SDYsrCrGLpSVCti/hQFMTUNOhilKiqrGKvh0KtZa3sGpHK1cqk613LhoadD21Yf81DZqmgzXoo+A+/TpA39/fwQFBSEoKAi+vr6IjIzEtGnT5G4aGYl69/OpfXbIydRtlzcxqRg53TGwEA5OSpSWCMjJNMXVGCtcPmtd77WWRIZSlgs4td8e/UflAACennsXfx+wR0kdg6GeeytJCsozh21x54ZhAezbpSrw05LMDKqLateiA3j+/PlyN4EakadvCbo+WCgtV594QxtXzzJ8vP5Gra/lZZlg2xpnbFjRGsWFPDSgxhP5iQd6DcyDta0KHQOLsGrPJaxb6o6Lp2yQlmyGVs7l8PUvwsRZqegWXAAAuHnZEl++7G3Qdi2tlRgwOltaPlPPZXvUMC06gKl5G/pk1dFvTqYJTu41zpeInaMST89LxYAxOVgY7os7N2peykFkDLeuW+Llxzri3cgbcPMqg6dvKV5bklhr2bxsE+z91RGRn3qgqMCwH4aTX0mBnWPFKbrCfAV2b9L9xyvpjgFMzZSIIU9kS0v7tziivKz+buOCPAWO7nBA9H47XDtvjfRkUyjLBbRyKUeXXoUYPTkDvQZWTGbQtkMJPlx3A3NHd9S5a5tIX/FxVnhugD8efioDz72VDKtqg7Mq/X3QDgd+dzQ4fHv0z8O459Ok5Q0r3Lh/NxJ+qtQsBYYWwN27auTmbh1GP2emmuLpngG1diunJ5vjyB/mOPJHK4x8JgNzPr0NhQLw8CnFtPnJWPJqW6O2n6iSvWM5nl+QhLDHs2BmLiLjriniom2Qk2UKW3slOvcsgHvbMgx6NBuDHs3GHz85Y8WbXlCp9J/HuXWbUry5MgEm/yTD+RM22Ph1ayO/I6rEAKZmSX3wVXycJa6dq38qvbJSBcp0uNpi5zpnuHmV4qm5qQCA4RMyEfmJO7LTOVCFjMvTtwSfbbwGV88ylBYLWDG/Df74yaXaTG4iBj1aMROWjb0Ko5/NgEoJrJiv349CO8dyfPDTdbRyruh6Tksyw8ezfBoU5KQbBrARJSYmIjGx9vMz9YmNjTVya1ouCysV+v0zchQA9jTCzFfrl7fGuBlpsLQSYWIK9BqYj32beYkTGY/CRMQ738XD1bNiMo5lb3ph98ba5j8XcOB3R+RmmuLj9RV3CHtkagZ2b3TC5bM2tZSvydJaiQ/W3oBPp4rJO3IyTTD/aT+kJ5sb5b1Q7RjARvTDDz/g3XfflbsZLV6/kTmwsas4T6YsR6MEY3GhCS6ftkb3fhUjT707ar9Gk0hfA0Znw9e/Yr+6dc2i3lH8pw/b4fQhW2mMwvCJmToFsJmFCu9GxqNLr4orBgryFFjwrB8Srxp2GRPVjxczUrOj3v3890E7ZKY2Ttewer32TuVaShLpr/egPOlxzDFbAPV3BZ89WjXSv2NgoZaSFUxMRbz9XYJ0l6TiIgELw9vz7kf3CAOYmhUXj1J0V7vlmj7X/uqrcso/ABpz8BIZg7N71TzQuVm6dVbmqo1WtrGvfbR0JYVCxH++TkDwP/M8l5UK+GCGL84dr3kvYWoc7II2ounTp2Po0KENWjc2NhazZs0ycotanrAnsqTZgPKyTRC1q/Hu3uWndo/WzLscgEXGVVpcdcRr10q3HhY7x6pyBVrmjhYEEa8sScTAMRVjJZTlwCcv+eDUPt7t7l5iABuRt7c3vL0Nm4GGDDNsfJb0+NDWVjrd+aUheg7IQ+s2VUcoFV2ERMaTeqdqAFT3vvlaSlbpodb7k5RQ9wCqOZ/cxtAnKv6vqFTA4le8ceSPVg1rKDUY+82o2ejcoxDeHatuwbZLjzsfmZqpYGqmvcuukoNTOeZ8cltavnnFAtfO8RaHZFzq0z96dyzBkCcytZQGuvfLQ9BDVeeNow/WfjQ7Y+EdjHo2Q1r++i0v7PmFM13JgQFMzcZQtcFXt65b4NJp3S7BAABnt3JERl3C+JmpaN2mrouBRfQZkotlO6/A07eijEoFfPeeJ0SR10qScZ3Ya49b16umOZ376S2MnpwOhUKsVlLEwEey8M538dIzqXfMcPD3VjXqnPxKMp6YUTXL1Xfve2L7WhdjN510xC5oahZMzVQY9M+9S4GG3ffX1bMMzy9IxvMLkpGSaI74S5bIzTRBeZkAB2cluvQsgIuH5rm4iA88eN6MGoVKKeDzud74bON1WFqrYGElYs4nt/HMvBRc/NsGuZmmsLZTwr9Xocasb6XFAj6Z5VPjjl0PhuXi2ZfvSsuZqaZw8yrFrA9uQxdrv3BHXjYjw5j4aVKzEDw0F/ZOFTP4KJXAnl8Mu/bX3btU40uturQkM6yY3wbHdzkYtB0ibS6fscHr4/3w2rJEtPWrOL3i7F6OAaNzai2ffNMcn83xxsXommMSWrmUaSw7tS7H2GnpOrfll1WuDGAja9GfZnx8PCIiIjSeU5+R6syZM1iwYIHG62FhYQgLC7sn7SPdDZtQNfgq5qit3jP43L1thhmDOyEgqBABDxbAp1Mx7J2UsHcsh4WVCoV5JshMNcOVGCuc2mePozsdoCxntzM1vstnbTBjcBeEDs9B34dz0DGwEM5uZbCyUaG4UIGsdDNcjbXC8V0OOPxHK+6XTYggimL1EwotxoEDBzB48GC91lm4cCEWLVpk9LYcOXIEAwYMkJZ7YxBaCTw3Q82QwICg5ilbTEe0uF9aPnz4MPr3719neQ7CIiIikkGL7oIeNGgQWnAHABERyYhHwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJwFTuBlCF/Px8jeU8ZAOiPG0halyC3A0gahR5YrbGcvXv9eoYwPeJGzduaCxfxll5GkLU2PjDklqI6t/r1bELmoiISAYMYCIiIhmwC/o+MWbMGI3l9u3bw9bWVqbWtAyxsbGYNWuWtPz1118jMDBQxhYRGRf38XsrPz9fo9u5+vd6dQzg+4S3tzdmzpwpdzNatMDAQPTv31/uZhA1Gu7j9xd2QRMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAd0OiFsvb2xsLFy7UWCZqTriP398EURRFuRtBRETU0rALmoiISAYMYCIiIhkwgImIiGTAAKYWS6lU4vz584iMjMTs2bMRGhoKa2trCIIAQRAQHh4udxOJdMb9uenhKGhqsSZMmIDNmzfL3Qwio+D+3PTwCJhaLKVSqbHs5OSEjh07ytQaIsNwf256eARMLVafPn3g7++PoKAgBAUFwdfXF5GRkZg2bZrcTSPSG/fnpocBTC3W/Pnz5W4CkdFwf2562AVNREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDLgdcDUbFy6dAmXLl2q8/UuXbqgS5cu97BFRER1YwBTs7F+/Xq8++67db6+cOFCLFq06N41iIhIC3ZBExERyYBHwNRsLFq0iEe4RNRk8AiYiIhIBgxgIiIiGTCAiYiIZMBzwNRixcfHIyIiQuO52NhY6fGZM2ewYMECjdfDwsIQFhZ2T9pHpA/uz00PA5harJs3b+LDDz+s8/XY2FiNLzAAMDU15RcW3Ze4Pzc97IImIiKSgSCKoih3I4iIiFoaHgETERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBE95lBgwZBEAQIgoADBw7UWiY8PFwqExkZeU/b11RERkZKn1F4eLjB9cnxmSckJEjbbNeu3T3ZZkMcOHBAauegQYPkbk6TwQCm+4p6+NT2z87ODu3atcOjjz6K5cuXIycnR+4mExE1CAOYmpT8/HzcvHkTW7duxZw5c+Dt7Y21a9fK3axmT/1HEBEZh6ncDSCqy4MPPog+ffpIy6IoIjs7G6dOncLVq1cBALm5uZg6dSqKi4sxY8YMuZpKRKQ3BjDdt0aNGoVFixbV+tqWLVswbdo0qQt6zpw5GDVqFLy8vO5hC+UTGRnJc79ETRy7oKlJGjduHNatWyctl5SUYOXKlTK2iIhIPwxgarJGjx6N7t27S8t79uyRsTVERPphAFOT1rdvX+nxjRs3NF5btGiRNHCosiu7qKgIERERGD58OLy9vWFubg5BEHD27Nla69+7dy9eeOEFdO3aFU5OTrCwsICnpydGjBiBFStWoKioSOe2qlQqrFmzBsOGDYO7uzssLS2lEd2//fabXu+7IZfEHDlyBHPnzkXPnj3RunVrmJmZwd7eHg888ACmTp2Kn3/+WeP9qF9aoq6uEeoJCQl1bvvWrVt4//33MWDAAHh6esLCwgJOTk7o2bMnXn31VVy5ckWv93/69Gn861//Qvv27WFlZQVXV1f06dMHn332GTIzM/Wqy9jKysrw119/4fXXX8fgwYPh6ekJS0tLWFlZwcvLCyNHjsSSJUuQn5/f4G3s3bsXTz31FPz8/KT3P2DAAKxYsQIlJSV61VVQUIBvvvkGjzzyCHx8fGBtbQ07Ozt07NgR06dPx759+xrcTqqHSHQfeeihh0QAIgBx4cKF9ZafP3++VN7MzEzjtYULF2rUdfHiRbFr167Sc+r/zpw5o7FuYmKiOGjQoFrLqv/z9PQUDx06VG87k5OTxeDgYK11jRs3TszNzdX4DPbv319rfVOnTpXKrF69Wuu2b926JQ4bNqze9wJADA4Oltbbv3+/TutU/ouPj6+xbaVSKb799tuipaWl1nVNTU3F+fPniyqVqt7P8q233hJNTEzqrMvLy0uMiooSV69eLT03derUeuutjy6feWJioujs7KzT5+Xs7Czu2rVL6zbj4+Ol8j4+PmJpaak4Y8YMrfX6+/uLly9f1uk9bdy4UXR3d6+3rWPGjBGzs7PrrEd9X3nooYd02jaJIgdhUZOWlZUlPXZwcKizXEZGBh5++GEkJibC0tIS/fv3h4+PD/Lz83H8+HGNsnFxcRgyZAiSk5MBVBzx9erVCwEBAbCyssKdO3dw6NAh5OXlISkpCcOGDcPOnTsxePDgWrednZ2NsLAwxMXFSc/5+voiNDQUFhYWuHDhAk6ePIktW7ZAoTBup9SFCxcwbNgw6b0AQOvWrdG3b1+4urqiuLgY169fx5kzZ1BUVITi4mKpXJs2bTBr1iwAwNdffy09X/lcdfb29hrLSqUSEydOxK+//qpRZ58+feDq6or8/HycOHEC169fR3l5OT766COkpaXh22+/rfP9zJ8/Hx9//LG0bG1tjbCwMHh4eCAlJQX79u3D7du3MWrUKMybN0+3D8mICgoKkJGRAQBwdHRE165d4ePjA1tbW5SWliI+Ph7Hjx9HcXExMjIyMGrUKBw8eFCjJ0ebN954Q/p8AgMD0aNHD4iiiL///hsXL14EULH/hoWFISoqCm3btq2zrq+++gqvvPIKRFEEUPH3Cw0NhZeXF5RKJS5cuIDo6GiIoojt27dj0KBBOHr0KKytrQ35iEidzD8AiDToewQcGBgolX/wwQc1XlM/AjY1NRUBiE8++aSYmpqqUU6pVIqlpaWiKIpifn6+6O/vL603cuRI8dq1azW2m5OTI7744otSOQ8PjzqPEKZPny6VMzc3FyMiImqUOXHihOjj4yOVqSxvyBFwTk6O2LFjR6mci4uL+L///a/Wo8z8/Hxx3bp14rRp02qtC2pHQ7p6++23pXXc3d3FX3/9tdZtb9y4UXRwcJDKbtiwodb6Dh48KAqCIJV78sknxczMTI0y2dnZ4qRJk2p8jvfqCDghIUGcPXu2eOLECVGpVNZaJicnR3zllVekujp16lRnWfUjYDMzM+nI+a+//qpRduvWraK9vb1UfsSIEXW+lz179ogKhUL6nD755BOxoKCgRrkzZ86IAQEBUp0vvvhirfXxCLhhGMB0X9EngLdv364RDP/5z380XlcPYADi8OHD6/yiq/Tee+9pdAnXV179S/mTTz6p8frly5c1QiMyMrLOui5fvixaW1trtNmQAH7rrbekMg4ODuKlS5e0vhdt9A3g+Ph4qZvYycmp1h8x6vbt26fRhVpbUPft21cqM2TIELG8vLzWupRKpTh8+HCNNt+rANbHCy+8INW3Y8eOWsuoBzAAUaFQiEePHq2zzt27d2uU37t3b40ySqVS44fZ5s2btbYzOTlZdHNzk34E3Lp1q0YZBnDDcBAWNUm//fYbnn32WWnZwsICM2fO1LrOkiVLtHbxlpWVYcWKFVJ9q1atqrdL+KOPPpIGKalfFlUpIiJC6uLr06cPpk6dWmddnTp1Mlq3aUlJiUa38SeffILOnTsbpW5dLF26FEqlEgDwzjvvwM/PT2v5wYMHY8SIEQAqulDPnDmj8XpcXByOHTsmLS9btgwmJia11qVQKLB8+fL7ftauadOmSY91HcH/zDPPaO2uHjp0KB5//HFp+bvvvqtRZtu2bdJENo899hjGjRundZvu7u7SfllWVoaNGzfq1FaqH88B031rx44dSE9P13guOzsbJ0+elL5AKi1evFjr+a7AwED4+/tr3V50dDRSU1MBAEOGDEHr1q3rbaOnpye6dOmCuLg4nD9/Hjk5ORrnovfv3y89njx5cr31TZ06FR999FG95epz/PhxZGdnAwDs7Oy0Bn9j2LFjh/T46aef1mmdsLAw/PXXXwAqRmz36tVLek39cwwKCkJAQIDWujp16oSQkBBERUXp02yjKisrw4kTJxATE4OUlBTk5eWhvLxcej0vL096XNco/OqmTJlSb5mpU6di8+bNADQ/t0oN/dtUOnLkCF5++WWd1iPtGMB03zp16hROnTqltYydnR2WLl2qcTRRm6CgoHq3p/5lffv2bbz00ks6tbMy6ERRxO3bt6UAFkURMTExUrnQ0NB66+rUqROcnJwMvpRGfWBZSEgIrKysDKpPHxkZGdJlRebm5nj33Xd1Wq9yEBFQcdmSOvUjYl0+x8pycgRwUVERPvroI6xatarGD8i66FJOEAQEBwfXW07987l79y6Sk5Ph4eEhPaf+mfz66684ePBgvXWq3/Sk+t+GGo4BTE2Kra0tnJ2dERgYiKFDh2LKlClo1apVveu5urrWWyYpKUl6HBsbi9jYWL3bpz4qOycnB6WlpdKyt7e3TnV4e3sbHMB3796VHrdv396guvSlPuK6tLRUoytcV+qfIwCkpaVJj/X5HO+1rKwshIWF6XxEW0n9aLgujo6OsLOzq7ecq6srLC0tpRHtaWlpGgGsvp9v2LBBr3YCNf821HAMYLpvLVy4sM65oPWlyxGgMW5tqN7FWH2iBV0v37CxsTG4Hepf6La2tgbXpw9jf46A5md5Lz9Hfc2aNUsKX3Nzc0yZMgWPPPII/P394eHhASsrK+ncdUJCAnx9fQFUTNJSH30u/7GxsZECuHq4G/r3qf63oYZjABP9Q/0Le86cOVi6dKlB9VUPvsLCQp1CoaCgwKDtAtA4UjJkxqWGUH+P9vb2Rglk9c+ysLBQp3WM8Tnq486dO1i/fj2AioFgf/75Z53XhgO6HfWq0/V9A5rvvfpRs42NjfQ3OX36NHr27KlXO8h4OAqa6B9ubm7S45SUFIPrc3BwgLm5ubScmJio03rGOMem/l7i4+MNrq+h287NzdUrOOqifgrhXn6O+ti3b5804n3kyJFawxcAbt68qVf9WVlZOv2YSk9P15hQxcXFReN1Y+/n1HAMYKJ/qA9wOXbsmPRl2lCCIGjcLKL6jFu1uXr1qjSTkiFCQkKkx1FRUXrNWW0oDw8PjRHp6pcPNZT6UZounyOAez4AS/3c6gMPPFBv+UOHDulVvyiKOHHiRL3l1N+3m5sbPD09NV5X38+PHj2qVxvIuBjARP/o16+fNKDr9u3b2LZtm8F1qh8F/fTTT/WWX7t2rcHbBCoC2NHREUBFV6eh9VpaWkqPy8rK6i0/ZswY6bExbhOp/jlGR0fj0qVLWstfu3btngew+jXj9R31FxYWNuhv8uOPP9ZbRr3e2o7C1f82P/zwg8bRMt1bDGCif1hYWGhMhDFz5kzcuXNH5/XVRx5Xeu6556THx48f1xrC165dw1dffaXz9rSpPjHJG2+8gcuXLze4PmdnZ+mxLp/JK6+8Ig022rJli853bAJq7xb19/fXmIBi7ty5dQ5cUqlUmDNnjsE9GPpSH22+Y8cOaSKS2rzyyiu17i/1+emnn7QeBe/fv19j7u3nn3++RpknnngCHTp0AFAxYn3mzJk6f1b5+fn3/Nx6c8YAJlLzyiuvoGvXrgAqgqZ3797YtGlTnV/26enp+Pbbb9GrVy98/vnnNV7v1KkTwsPDpeXnn38ea9asqVEuOjoaw4YNQ0FBgcZ5Y0O8/vrr0gxUOTk56N+/P9avX1/rl21hYSF+/vlnTJ8+vda6unXrJj3etGlTvdv28/PDggULpOXp06fj1VdfrfN61/LycuzatQuTJ0+uc1DQhx9+KM1utWvXLjz99NPSNdiVcnNzMXnyZOzcudNon6OuwsLCpJHK165dw9SpU2tt34wZM7Bq1Sq9R2mbmZlBqVRizJgxtc6c9ccff2DcuHHS33fYsGEYMmRIjXImJib45ptvpB9Iq1evxujRozVuFlLd2bNn8cYbb6Bt27b3fExBc8ZR0ERqbG1tsXXrVgwdOhTx8fFISUnBhAkT4OLigpCQELi7u0MURWRmZuLixYu4evWqFM7qswWpW7x4MaKionD58mWUlJQgPDwc7733Xo27IYmiiMcffxwZGRk6TY5QH3t7e2zevBnDhg1Damoq0tPT8dRTT2HevHk17oZ0+vRpFBUVaZyzVvfEE09Is1S98cYb2LlzJ7p27QoLCwupzFtvvSV1ewMVl5ElJCRgzZo1EEURX375JZYvX47evXvDz88P1tbWyM3NRUJCAmJjY6UjK/WjbXWDBg3Cq6++Kv3Q2bBhA7Zv346wsDC4u7vj7t272LdvH/Lz8+Ho6Ii5c+ca7TI2XTg6OuLVV1/Fe++9B6BiatKdO3ciODgYbdq0QXJyMg4cOICCggKYmppi5cqVes1Q5unpiXHjxmHJkiUYNmwYunfvrnE3pAsXLkhlPTw8ap2GstLQoUPxzTff4MUXX4RSqcTOnTvx559/IiAgAIGBgbC3t0dhYSGSk5MRExOjcR02GZEcE1AT1UXfuyFpU/1+wPrIyMgQx48fr3EjBW3/WrVqpfVGC3fu3BF79+6ttY6xY8c2yv2AExISxIEDB+r0Pvr161drHaWlpfXWUdv9gEVRFJctWyY6OjrqtH1BEMSxY8dqfT//+c9/pDv51PbP09NTPHbsmCz3Ay4vLxenTJlS776yZcuWGvf6rU1t9wN+7rnntNbfuXNnMS4uTqf3tG/fPo0bM9T3r2vXruKdO3dq1MObMTQMj4CJauHk5ISNGzfi/Pnz+Pnnn3HgwAHEx8cjIyMDCoUCrVq1QocOHdCrVy8MHToUw4YN0xioVJ2npyeOHz+OtWvXYt26dYiNjUVOTg7c3NzQvXt3TJ06FU888USj3EDAx8cHBw8exN69e7Fp0yYcPnwYycnJyM3NhY2NDXx8fBAUFITRo0dj7NixtdZhZmaGPXv2ICIiAr/++ivOnz+PzMxMjZm+6jJ79myEh4fjxx9/xO7du6UjquLiYtjZ2cHLywtdu3bFoEGDMGrUKK1zegPAxx9/jCeffBIrV67Evn37kJycDFtbW7Rr1w6PP/44ZsyYARcXF4POeTeUiYkJ1qxZg/Hjx+Pbb7/FiRMnkJWVBUdHR3h7e+PRRx/F9OnT4enpiYSEBL3rNzMzw/fff4/x48cjIiICp06dQnJyMmxsbODv74+JEydixowZGj0T2gwePBhxcXH47bff8Mcff+D48eNISUlBbm4urK2t4ebmhi5duqBv374YOXIkevTooXebqW6CKN7jkQpERETEQVhERERyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDP4fICrVMXSf1c0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "sns.set_context(\"poster\")\n",
    "\n",
    "disp = plot_confusion_matrix(tree, X_test, y_test, colorbar=False)\n",
    "_ = disp.ax_.set_title(\"Decision tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification using bagging classifier with and without sampling\n",
    "\n",
    "Instead of using a single tree, we will check if an ensemble of decsion tree\n",
    "can actually alleviate the issue induced by the class imbalancing. First, we\n",
    "will use a bagging classifier and its counter part which internally uses a\n",
    "random under-sampling to balanced each boostrap sample.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from imblearn.ensemble import BalancedBaggingClassifier\n",
    "\n",
    "bagging = BaggingClassifier(n_estimators=50, random_state=0)\n",
    "balanced_bagging = BalancedBaggingClassifier(n_estimators=50, random_state=0)\n",
    "\n",
    "bagging.fit(X_train, y_train)\n",
    "balanced_bagging.fit(X_train, y_train)\n",
    "\n",
    "y_pred_bc = bagging.predict(X_test)\n",
    "y_pred_bbc = balanced_bagging.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Balancing each bootstrap sample allows to increase significantly the balanced\n",
    "accuracy and the geometric mean.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging classifier performance:\n",
      "Balanced accuracy: 0.73 - Geometric mean 0.68\n",
      "Balanced Bagging classifier performance:\n",
      "Balanced accuracy: 0.86 - Geometric mean 0.86\n"
     ]
    }
   ],
   "source": [
    "print(\"Bagging classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_bc):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_bc):.2f}\"\n",
    ")\n",
    "print(\"Balanced Bagging classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_bbc):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_bbc):.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(10, 5))\n",
    "plot_confusion_matrix(bagging, X_test, y_test, ax=axs[0], colorbar=False)\n",
    "axs[0].set_title(\"Bagging\")\n",
    "\n",
    "plot_confusion_matrix(balanced_bagging, X_test, y_test, ax=axs[1], colorbar=False)\n",
    "axs[1].set_title(\"Balanced Bagging\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification using random forest classifier with and without sampling\n",
    "\n",
    "Random forest is another popular ensemble method and it is usually\n",
    "outperforming bagging. Here, we used a vanilla random forest and its balanced\n",
    "counterpart in which each bootstrap sample is balanced.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=50, random_state=0)\n",
    "brf = BalancedRandomForestClassifier(n_estimators=50, random_state=0)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "brf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_pred_brf = brf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the previous experiment, the balanced classifier outperform the\n",
    "classifier which learn from imbalanced bootstrap samples. In addition, random\n",
    "forest outsperforms the bagging classifier.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest classifier performance:\n",
      "Balanced accuracy: 0.73 - Geometric mean 0.68\n",
      "Balanced Random Forest classifier performance:\n",
      "Balanced accuracy: 0.88 - Geometric mean 0.88\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_rf):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_rf):.2f}\"\n",
    ")\n",
    "print(\"Balanced Random Forest classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_brf):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_brf):.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(10, 5))\n",
    "plot_confusion_matrix(rf, X_test, y_test, ax=axs[0], colorbar=False)\n",
    "axs[0].set_title(\"Random forest\")\n",
    "\n",
    "plot_confusion_matrix(brf, X_test, y_test, ax=axs[1], colorbar=False)\n",
    "axs[1].set_title(\"Balanced random forest\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boosting classifier\n",
    "\n",
    "In the same manner, easy ensemble classifier is a bag of balanced AdaBoost\n",
    "classifier. However, it will be slower to train than random forest and will\n",
    "achieve worse performance.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from imblearn.ensemble import EasyEnsembleClassifier, RUSBoostClassifier\n",
    "\n",
    "base_estimator = AdaBoostClassifier(n_estimators=10)\n",
    "eec = EasyEnsembleClassifier(n_estimators=10, base_estimator=base_estimator)\n",
    "eec.fit(X_train, y_train)\n",
    "y_pred_eec = eec.predict(X_test)\n",
    "\n",
    "rusboost = RUSBoostClassifier(n_estimators=10, base_estimator=base_estimator)\n",
    "rusboost.fit(X_train, y_train)\n",
    "y_pred_rusboost = rusboost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Easy ensemble classifier performance:\n",
      "Balanced accuracy: 0.84 - Geometric mean 0.84\n",
      "RUSBoost classifier performance:\n",
      "Balanced accuracy: 0.84 - Geometric mean 0.84\n"
     ]
    }
   ],
   "source": [
    "print(\"Easy ensemble classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_eec):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_eec):.2f}\"\n",
    ")\n",
    "print(\"RUSBoost classifier performance:\")\n",
    "print(\n",
    "    f\"Balanced accuracy: {balanced_accuracy_score(y_test, y_pred_rusboost):.2f} - \"\n",
    "    f\"Geometric mean {geometric_mean_score(y_test, y_pred_rusboost):.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\envs\\MachineL\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ycrLx22+/Gf/+979zfd/eu5aF6bVkb53W/79NmjTJ9finp6cbH330kfm9x93dPc8eVtZ/Sw8PD8NisRhTp07N8b+TkpKSb/vtRRIKp7D+4F599dXGmDFj7P5xpIuqYRhGTEyMmTTUrFnTSE9Pz1HGuivt559/XuRtff3112Y93bt3z7fswoULzbINGzbMt6uoPay/UISFhRXqGOeVDFqfFCUZzz77bJ7bj4qKskm2cqvTWcc5IiLC7FoSEhKSa8JkbdmyZeZ2mzVrluuxvnJfJ02alGd9X331VY7uJ/kFjZdfftkmwcvLlXV+8803eZbdvXu3GTQkGTNnzsy1nD2B7aWXXjLLDB06NEcinV+dBXU9Ayq6wiShU6dOtTkH/PXXX/mWL+4k9PDhw7k+ZuDr62v06NHDePzxx42vv/7aiIyMLNS2rdvdr1+/HDHpwQcfNIYNG2Y0atTIZrsPP/xwrjE826xZs8yynp6e+Xbb3Lhxo00309wuEGZkZBj169c3y9x+++15xurMzEzjpptusonrV55Lf/nlF/P9u+++286jlbviTEIXL15sc9znzZtX5Lqc3c7rrrvOrG/Pnj053nfWMa5SpYohZV2Ujo+PL3I9JZWErlq1yuazeO7cObvrvPbaa3MtY/23lGS8/PLL+dbpKJJQOMWVH9zC/ERERDi8feu7Nzt37szx/r/+9S/z/QULFhR5OykpKTZXbPft25dnWetg9corrxR5m9kK82yBvQmU9UmxatWqBV79HjZsmFn+/fffz/G+s47zuHHjzHree+89u9YZNGiQuc6WLVtyvG+9r9WqVcv3Sl5SUpLh5eVllm/btm2+2z58+LBZtkqVKnmWs/6bXHPNNQXu0xNPPGGW79KlS65lCgpsqampRrVq1cxge+bMmQK3e/LkScNisRhS1l1YAHkrKAmNjY01Vq1aZdx5550254D87vhlK+4k1DCy7lLVrl27wDhSv359Y+LEicbJkycL1W57fpo0aWLXc+idO3c21xk7dmyB5a2/G+R2Dv3jjz/M9728vIzTp0/nW9+JEycMT09Pc50///zT5n3rC9Xjxo0rsH35Kc4k9NprrzXrHj58uEN1Obud1nemc/ue4axjnP13DA4OdqS5JZaEWt/Jtff7VtOmTQ1JhsViyXUcFeu/ZVhYmJGWlmZXvUXF6LgoE86ePauFCxfq9ddf1zPPPKNHH31UjzzyiPmT/QydJG3fvj3H+nXq1DFff/7550V+rsHb21v33XefuTxz5sxcy505c0a//fabJMnd3b1MTDp8ww03yMfHJ98y7dq1M19HRkbmeN9Zx/n33383X9911112rdO3b1/z9Zo1a/Ite8MNN+T67HA2X19fm2dRbrvttnzra9Cggfz8/CRljaAbHx9fYHutP0d5GTFihPl606ZNdo0YeaXNmzeboxr269dP1apVK3CdsLAw83mV3bt3m88DA8jfiy++mGPUy0qVKqlnz56aN2+eJKlKlSp67bXX9M4775Rwa7N07txZe/fu1auvvqqGDRvmWS4iIkLTpk1T48aNnT7S+4EDB9S/f3+NGzfOfDb0SvHx8TaxfvTo0QXWe//995uvczuHLlu2zHw9ePBg1ahRI9/6atWqpWuvvdZcXr58uc371jHwxx9/dHhE2eJw6dIlc+RTSXr00Udduv2kpCQtW7ZM06dP16RJk/TYY4/ZfJ/L/j+RCv4+58gxzq7n4sWL+vbbb4tUR0lJT0/X4sWLJUlBQUEaMmSIXetlj/FgGEaBsx3cdtttdo1v4QgGJoLTTZ48WVOmTHFKXXv27NHTTz+tP/74w+6E5vz58zl+d9ttt2nKlCnKzMzUb7/9ppYtW2r06NG67rrr1KJFC5vJwwvywAMP6L333pMkffnll5o2bVqOf9S5c+eaD7sPHjxYYWFhdtdvj169etkEEWdo1apVgWWsh9DPbfoQZxzn6Ohoc8oVLy8vc/CegljPK1fQMPotW7YssL7g4GDzdYsWLewqnz14QVxcnAIDA/Mt37Vr1wLrbNWqlQICApSQkKCMjAzt3LnTrvWsWQ86ceLECT3yyCN2rZc9WIlhGDpx4oQqVapUqO0CyMnd3V2vv/66/u///q+km2IjICBAzzzzjJ555hnt2rVLK1eu1IYNG7Rt2zbt3bvXZiqtpKQkPfbYY4qOjrbr/Dx79uwcF2INw1BCQoIOHjyoBQsW6J133lFiYqKmT5+uHTt2aNGiRfLy8rJZZ+fOneb3gICAALVu3brAbbdt21b+/v5KTExURkaGduzYoW7dupnvWw+wYv37/HTv3l2//PKLJGnr1q0273Xp0kV16tTR8ePHdezYMbVo0UKjRo3SDTfcYA4+V9K2b99uJvp+fn7q3LmzS7Z74cIFvfDCC/ryyy/tulAr5f59zlnHeNiwYXrttdckSXfeeae+/fZbDR8+XH369LHrYm1J2rlzp3lBxdPTU4899phd623atMl8XdD3pA4dOhS9gXYiCUWptWjRIt10000285jZI7eTW7NmzfTGG2/oySeflGEY2rdvn5566ik99dRTCg4OVrdu3dSrVy/ddNNN+Y6gl11Xjx49tGbNGp05c0a//vqrbr75Zpsy1qPiWl+JLc3sSTI8PT3N19YTL2dzxnG2HjU3NTXVZsRCexU0Z6c9+2p9YaGw5XM7NlcKDw8vsIzFYlHt2rXNEfWsR4O0l/VInDt37tTOnTsLXUdR50AFKpqrr75anTp1MpcTEhJ07NgxrV27VpcuXVJGRobuv/9+HTlyRK+88koJtjRvrVq1UqtWrcwLVhcvXtRvv/2m6dOn29yJnDp1qm644YZ8R7LNi8ViUWBgoNq3b6/27dvr9ttvV/fu3RUfH68VK1bo1Vdf1eTJk23WsT7/1alTx66Lmm5ubqpTp455Dr0yqbGu0955UuvVq2e+vrI+T09P/ec//9GQIUOUkJCg8+fP680339Sbb75pjvrbs2dPDR48WN26dSvUBXBnOXPmjPm6Tp06xX63S8qaf7Vnz546duxYodbL7fucs47xpEmTtGLFCq1fv16GYeinn37STz/9JElq3LixrrnmGvXr10833HBDgReVXc06rkdHRxfL96SqVasWus7CojsuSqVz585p+PDhZgJat25dvfrqq1qzZo1OnTqlpKQkZWZmysh6rtkmWFlfsbU2fvx4LV++XP369bM5KWUH2KeeekpXXXWV+vfvbw7vnpcHHnjAfH1ll9w1a9aYw4LXrFlT119/feF2voQ4Kxg6epyd0fUz+y50Xgq7r8XxRSG7+25BrKcBsvfqsTVXHE8AWQYPHqwZM2aYP3PmzNGyZcsUGRmpO++80yw3bdo0zZ8/v8D6rBOE1NTUQrXF+gKu9QXEwgoODtY999yjDRs2aMKECebvDcPQBx98UOR6rbVq1cqmW+j777+fY3+zp/iQVOD0aNbyO4cWpc6Czsm9evXSjh07dN9998nX19f8fUpKitasWaNp06apR48eatq0qRYsWGDvbjiNdZsDAgJcss277rrLTEADAwP1+OOP688//9SRI0fMnj7Z3+esuzjn9X3OGcfY399fK1eu1JtvvmlzYUGSDh48qFmzZunuu+9WjRo19NRTTyk5Odmxg+BErojr1se1uJCEolT6/PPPzX+yNm3aaOfOnXrmmWfUvXt31axZU76+vjaJgb1fznv16qUlS5bo9OnT+vbbbzV27Fi1b9/eZl60pUuXqnPnzvn2l7/tttvM7pp//PGHzVUp66R05MiRcnd3t2+nyxFHjrN1gA8KCjIDU2F+5syZ46pdLTJ75x2zfoapKFdjrY/n2LFji3Q8e/fuXejtArisRo0a+uqrrzRo0CDzdw899FChem1YJ0z2sC5fuXLlQq2bGzc3N73++us28xuuXr3a4XqzWR+bCxcu5JiL0DphKszz8fmdQ4tSpz3n5AYNGmju3Lk6d+6c/vzzT02aNEl9+vSx+WJ/4MABDR061OXPBlu3ubCfqaJYu3at1q5dKynreK9fv17vvPOOBg0apPr168vf39/mu4G93+eccYy9vLw0YcIEHTlyRNu3b9f777+vO+64w2ZO1qSkJL355pvq06dPqUlEreN669atixTXnfXYnCNIQlEqLV261Hw9adIkBQUF5Vv+6NGjhaq/evXqGjZsmKZPn64tW7YoKipK7733nvnMY3Jysv7973/nub6vr6/uueceSVJGRobmzp0rKevk+d1330nKuntW2p77cbWiHGfrydvj4uKKPEl0aWdPtyTjf5OFZwsNDS30dqyPZ1RUVKHXB+Acbm5u+uKLL8wvkBcuXNC0adPyXce6S1xEREShtnfkyJFc63GEm5ubBg4caC5bPz7hqJo1a9osXxnXrffhxIkTMgyjwDozMzNtnn278hxqXae9XUWtB+Ur6Jzs7++vQYMGaerUqVq2bJmio6P13Xff2YzB8Oyzz9qc54ubdUw4fvx4sfd0sf4+N2LECDVv3jzf8oX9PueMY2yxWNSmTRs9+uijmjdvnk6cOKGtW7dq1KhRZpkNGzYUqdtrcSgvcZ0kFKWS9Z3FggbMycjIKHCUr4JUrVpVjz32mH7++Wfzd//8849NEL+SdZfc7GdAv/nmG/Mqaa9evfIdabAisuc416xZ02b0u+wrqOXN+vXrCyyze/du86qwu7u72rRpU+jtWA86sXbtWru+uAEoHrVr19a4cePM5RkzZuT7JdJ6cBDr5zELkpmZaTNojjMHGbEeRT2/UcYL68oLjtZ3x6SsOz7ZPYvi4+MLfGxGknbs2GHG5NzOodYjvtsba6zLtW/f3q51svn6+uq2227TihUrzEQiNTVVixYtylG2uJ4Xbdu2rfk3TEpK0oYNG4plO9kK831OklatWuXQ9gpzjPPTrl07zZo1y2Zcj4ULFzrUNmdp27at+b939uxZHTp0qIRbVDQkoSiVrINPQXfCFixY4LQrQd27d1dISIi5bP0A/5VatmxpjlR66NAhrVy50qYrblkZkKgkFHScrYcb/+ijj1zWLlf66quvCizz5Zdfmq+vvvrqQj0Hla179+5mV7wTJ06YozoCKBnjx483u0SmpKTojTfeyLNs9pQKUtadGHvvhi5fvtwmLvbq1auIrc1px44d5mt7Bliz15UjzVp3iZSyupFaD4Jkz2MX1jG5U6dOOc6h1lN7/f777wVO93Hq1Cn98ccfua5fGCEhIerevbu5nNt3Detk357B7uzl7e1t87maMWOG0+rOTWG+z506dcrmIrUj7DnG9rjxxhsdrsPZfH19bT57ZfV7EkkoSqUGDRqYr/O78nTu3Dk9/vjjBdaX2zDfuYmJibF5RqKgYbqt74Y+/fTT5hXF4OBg3XrrrXZtszxx1nEeP368ecX7p59+KtQznmWla8qKFSv0/fff5/n+3r17bb4cFPWihre3t82dl4cffrhQXb9KS9AFyovg4GCbQXg+/fTTPEe+7tevnzmSuGEYGjduXJ6DtWRLTk62GUBo4MCBNvMeZ0tNTdUjjzxSqPPBypUrzfkJJdnMmemItLQ0m/NdUFBQrqPuWj++8eGHH+Y72veWLVv06aefmssPPvhgjjIDBw5U/fr1JWUN5GR9rrySYRh69NFHzYSwYcOG6t+/v02Z6OjoPNe/knU34dy+a1hPiebs7rpPPPGE+fqbb77RN99849T6rdn7fS4jI0MPPPBAgQNwOeMYX7p0ye7nYQv6O5WUp59+2nz9wQcfaMmSJXavW1q+J5GEolS64YYbzNevvvpqrneNtm7dql69eun48eMF3iEaNmyYhgwZou+//z7PK3EnT57UXXfdZZ4AmzRpUmB32mHDhpkDR1h3abn77rttrmJWFM46zg0bNtSkSZPM5dGjR2vChAl5Jrnp6en666+/dO+999p0ryrNvLy8dN9999lMzJ1t3bp1GjRokDmXW4sWLXTvvfcWeVvjx4835zo9efKkOnbsqO+++y7PL7Pnz5/XZ599pvbt2+vNN98s8nYB5O6JJ54wB8VJSkrS22+/nWs5i8Wi119/3VxeuHChbr31VpvnEq3t3r1bAwYM0Pbt2yVljYr78ssv51o2MzNTH374oRo2bKi7775bixYtynNKtJSUFH366acaMmSIed7w9/fX2LFj7dndfJ0+fVq33367zZ3QRx99NNcRfe+++26zS21qaqoGDRpkM5pqtiVLlui6664zn3ds3769zejE2dzc3My5IiVp3rx5+te//pUjQYmPj9eoUaP0448/mr974403cnQZ/uCDD9S2bVt9/PHHeX7RT0hI0HPPPWfO2eju7m7znG026/msv//+e6c+StG/f3/dfvvt5vI999yjl156Kde4nZmZqeXLl2vo0KFFGpX1+uuvN7sWr1ixQhMmTMgxwE9UVJRuvfVW/fbbbwV+n3PGMT59+rTq1KmjCRMm5NvNffHixTazL1x33XX576wL9erVSyNGjJCU9R3o+uuv16uvvppncp2SkqIFCxbopptusrm7W5KYJxRO9/vvv9t9R0zKmqriyu5II0aM0Ntvv60DBw7o0qVLuvfeezVt2jS1adNGPj4+2r17t3niaNOmjQYNGpRvl6bMzEz99ttv+u233+Tl5aUWLVqoSZMmqlSpkuLj43Xs2DGtW7fODK7u7u6aPn26XW2/++67c3SFKO6uuAcPHjTncLPXs88+m6N7k7M58zhPnjxZkZGRmjt3rgzD0Ntvv60PPvhAHTt2VMOGDeXn56e4uDhFRkbaTNxsffW4NHvjjTc0btw43XXXXZo8ebI6d+4sT09P7d6922ZC6YCAAM2dO9ehSc4DAgK0cOFC9e/fXxEREYqKitKwYcMUGhqqLl26qEaNGjIMQxcuXNCePXt08OBB829U1O5mAPJWpUoVjRkzxkwwP/zwQz311FM2jylku/nmm/Xcc8+Z84ouWLBACxcuVLt27XTVVVcpMDBQcXFx2rlzp/755x9zPTc3N82YMUNXX311vm25dOmS/vvf/+q///2vvLy81K5dO9WtW1fBwcFKTU3V0aNHtWnTJptRSz08PDRr1izVrl27wH396quvcv2in5iYqAMHDmjjxo02g+N069ZNEydOzLUuLy8vzZs3T7169dK5c+cUFRWlvn37qk2bNmrbtq0kafv27TZdhqtVq6Z58+blOU3NsGHDtGrVKnPQmS+++ELffvut+vTpo+rVq+vs2bNaunSpzZf7cePG6ZZbbsm1vh07dujhhx/WmDFj1LBhQ7Vs2VKhoaFKS0vT6dOntXbtWpu6nnnmGZtxELLdcsstmjhxogzD0G+//abWrVurW7duNqPb3nHHHUWapzV7P48ePaqNGzcqIyNDkydP1htvvKHu3burTp065sB4mzdvNu8+FiURbtq0qe69917z8ZK3335b//3vf3X11VerWrVqioyM1KpVq5SamqrAwEC9+eabud61tuaMYxwTE6O3335bb7/9tkJCQtSuXTvVqlVLPj4+Onv2rHbu3GkzXkWTJk302GOPFXr/i9Onn36q06dP66+//lJqaqomTpyol19+WZ07d1Z4eLi8vb0VExOjw4cPa/fu3eZFJmc+I+4QA3CCXr16GZKK9FOpUqVc69y/f7/RoEGDfNft3r27ceLECWPy5Mnm7yZPnpyjriFDhtjdnmrVqhkLFiywe9+3b99us37Hjh2LeBTzZ72PRfnZtm1bjjpHjBhhvj979uwC2zB79myz/IgRI3K8XxzH+f333zeCg4PtqtNisRg33nhjrvUUdl+tP9PLly8vsHzdunXN8hEREbmWsW6rYRjG888/b1gsljz3JywszFizZk2+2y3MfkVHRxu33357vtu0/qlcubIxZ86cAvcdqMiszxW5xZ+8nD171vD39zfXnTRpUr7lZ8+ebYSGhtr1v1unTh3j119/zbe+tLQ049Zbb7Vpgz0/TZs2LfCcaH0+LMzP6NGjjbi4uAKP3f79+4127doVWF/79u2NQ4cOFVifYRjG1KlTDW9v73zr8/HxMaZNm5ZnHW+99Zbd++rl5WW8+OKL+bbp2WefzbcOe2JZfpKSkox//etfhru7e4Ht9fHxyfVvY0+sTExMNAYOHJhv/bVr1zbWrFljLF++3Pxdr169ctTljGN84sSJAv/W1j+9e/c2Tp8+neu+FfS9KFthYrW9dRqGYaSnpxvPP/+84efnZ9e+eHp6GmPGjMm1rsJ+73EUd0JRajVp0kTbtm3Thx9+qB9//FH79+9XamqqatSooVatWumuu+7SsGHD7JqHc+HChdq2bZuWLl2qDRs2aO/evTpx4oQSExPl7e2tqlWrqnXr1ho8eLDuuuuuAqeEsdamTRs1aNDAvGJWkQckKo7j/Oijj2rkyJH6z3/+o8WLF2vHjh06d+6cUlJSFBgYqNq1a6tFixbq3bu3Bg8enOsV5dLqpZde0uDBg/XZZ59p9erVOnXqlDw9PdWoUSPdcsstGjNmjM08gY4KCQnR/PnztXv3bs2bN08rVqxQRESEoqOj5ebmpsqVK6tRo0Zq3769+vfvrwEDBlTIbuWAK1StWlUPPfSQ3nrrLUlZ3QzHjx+f55yeI0eO1LBhw/T1119r6dKl2rJli86fP6+EhAQFBgaqevXquvrqqzVo0CANGzYszzt/2Tw8PPT9998rOTlZa9as0erVq7Vt2zYdPHhQUVFRSkhIkLe3t4KCgtSwYUO1a9dON910k/r27ZujG2pR+Pr6qnLlymratKm6deume++9V1dddZVd6zZp0kSbN2/W999/rx9++EEbN240BxWqVq2aOnfurNtuu0233nqr3aPMTpo0Sffee6+++OILLVq0SBEREYqJiVHlypXVoEEDDRo0SPfff3++gzGNHz9et956qxYvXqy1a9dq165dioyMVFxcnHmObdasmfr27av77rtPdevWzbdN06ZNU48ePTR79mxt2bJFZ86cceq0Zb6+vvrss8/0xBNP6Msvv9TSpUsVGRmpCxcuyMvLSzVr1lTr1q01YMAADR8+vEhzVUtZvcb++OMP/fe//9XcuXO1bds2xcXFKTQ0VA0aNNCtt96qkSNHKjg4WCtWrMi3Lmcc41q1aik6OlrLli3T6tWrtWXLFh06dEjnzp0z78jWrVtXV199tYYPH57j2d/SxN3dXS+99JIeffRRffnll1qyZIn27Nmj8+fPKy0tTUFBQapbt65atWqlPn36aPDgwU6bsslRFsNgvH7AEZGRkWrQoIEMw5C/v79OnTpVqCQWAAAAqEgYmAhw0KxZs8znJIYNG0YCCgAAAOSDO6GAA1JSUlS/fn1zhLYNGzaoU6dOJdwqAAAAoPTiTijggEmTJpkJaLdu3UhAAQAAgAIwMBFQCH/++af+/PNPJScna+PGjeZcbBaLRa+++mrJNg4AAAAoA0hCgUJYv359rvNaTpgwQT179iyBFgEAAABlC0loKXHs2DH9+uuv5nKDBg0UEBBQgi1Cbo4dO2a+9vHxUYMGDTR06FBde+21WrNmTQm2DEBCQoLN5OJDhgzJdzoFwBpxGAAcU5g4TBJaSvz6668aM2ZMSTcDhZCSkqI9e/Zoz549euWVV0q6OQBy8fDDD5d0E1BGEIcBwPnyisMMTAQAAAAAcBmSUAAAAACAy9Adt5Ro0KCBzfIH00LVurl3CbUGyNsTk64r6SYAuUpIjNL+CNtn+gB7EYdRVjzxPHEYpVNCYpT2H7EvDpOElhJXDn7Qurm3enT2LaHWAHmrHFS3pJsA2IVBZVAYxGGUFcRhlBX5xWG64wIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABcxqOkG4DyIyNDOrrfRwe2++nATj8d2O6niL0+Sk/LutbRumuC3vzhkN31xce4a9vqAO1YG6jDu311KtJLiXHu8vI2VKlKupq0SVKXgXHqecNFeXjaV2dmprR3s7+2rQ7Qvm3+OnbARzHR7srMsCigUobCm6SoTbcEXXtntKrUSLerzr++DdHbj4fbvV+SdO2d0Xr87eOFWgeuU71qgtq3OaXWzc+oXt0YVQtNlK9PmpKSPXX+gp/27q+qZavra9eeGnbV5+OdpjYtz6hdq9Nq1OCCaoXFKTDgkjIz3BQb763DESHatLWWlq6qr5RLBX+Y7x22XfcO31nk/bv3wVt05lxAkdcHUDqVhTgsSYlxbjq400/7t/vp4P/aeeaEl/n++HePaeDwC/ZX+D+GIW1dGagVP1fW/u1+On/aS8mJbvL1z1SV6mlq3DpJ1wyJUaf+cXJ3L3T1cCGbOByeRxxeY18cdnfPVJOG0Wre5Jwa1LugOrXiVDU0UQF+qXJ3z1RikpfOnAvQ/oNVtPzv+tq9t7rD7ffxSdPn7y5U9aqJ5u/+M7+1/jO/rcN1lxckoXCKtX9U0muPhOtSsuNn9eREN736UF1tXRWotNScN+vT06SkBHedPuqtlQuDNef1Gnpy+jG16pKYS22X/fBpVX3/cTVdOJt7pLx4zk0Xz3lqx9+B+u971XXn2DO6c9wZAlUF0rB+tB57YIOaNjmf6/tBgakKCkxVg7oxun7gQW3fXV1vftBd587nndA9PXa1unc+Lh+fXC5qeGbKxydd1asmqlun4xpx5zZN/7SL/t5Q11m7lENqmpviE7wKLgigTCkLcViSXn2orlYurCzDsDjcTmtRx730xqPh+mdjzvNxYpy7EuPcdeygj5b+EKJGrZL09IyjCm98yaltgOPMONy4EHF4Rv5xuNvVx/X8hJV5vl8p6JIqBV1Sk4bRuuHaA9q6s4be/qhbvnUWZPRdW20SUOREEgqnSIhzd0rgk7KC34YllWx+F1w1TY3bJCmkarrS0y06/I+vIvb4SpLOHPfW08Ma6YWZEeoyIC7PeresDLRJQD29M9WkTZKq106Vt2+mzhz30j8bA3QpxU3paW76z9s1deKIt5764Jjc7Oy4XqdRitr2iC+wXPOOnJhKozphcTkS0OMngxR5vLLi4rzl75+qFledU9XQJElS25ZnNH3aH3ri+WsVdSYw1zqv6XpUXl6Z5nJcvJf2HwpV9AVfWSxSeO1YXdXovNzcpMqVLmnyUys1/dPO+u2vq/Js575Dofr5j7zfv9KgPofNJHjD5tpKSiYJBcqbshCHJenCWU+nJ6AXznroqVsb2dxNDa2ZqgbNkxVSLV3RZzx1ZI+PoqOy3j+0y08ThjbW9N8OqGbdVKe2BY6pExaXIwEtMA6/8r84fDb3OGwtNc1Nx09U0qmoQMUneEsWQyGVU9S08TlVrpR1UaJ96yi9O/VPPf78tUVKRJs1OacbBh0o9HoVDUkonCq4apqatElSk7ZJuqptkjavCNKCL6oWqa6Ayunqf9tFDRwerYYtUnK8v3uDv958LFxRx7yVkW7R64/U1aw1exVcNe9utBaLoav7xunauy7o6j5x8vIxbN6PjXbXR8/X1ooFwZKk5T+FqEWnRN0wItquNjdtn6RHpp0sxF6iNDp5OlB/LGmspasaKPqCn817FouhgX0Oacz/bZKPT7pCqyTrmcdWa9zE6yTl/sUqJcVDy9fU06JljbT3QNUcX8Dq1onR02NXq1GDi5KkR+7fqH/2VVPkseBc69u0tbY2ba1t177UrROjm67bby4vXtHQrvUAlE2lPQ5LkpuboTqNU9SkTVYbm7RN0tT76+vcqaJdIJs1LcxMQD29M/Xw1JMadEe03K2+5aanSb9/FarPXgxTWqqbYi946JMXaunFuRFF2iaK18nTgfpjaQFxePQVcfi53ONw9EVfff19K23aVksHD1dRWnrOizVubpka0PuwxvzfRvl4Z6ha1SQ9+q8NeuHVfoVqt4dHhh5/aK3c3Q2dPB2ouHhvNcujd1VFRxIKp+jYJ07/2fiPqtVOs/n9vq3+ha7Lw9PQ3Y9H6dYHz8o/MDPPci07J+r17w7rof5XKSneXUnx7vrp86oaPfF0ruXb94zX/z13KtdAmq1SlQw98+FRXUp207pFWVeB//teDQ25L1oW5164RSkUfdFXb87opqUrGygzM/fb34Zh0aJljZWQ4K3JT6+QJDW/6rw6tD2lLdtr5Sj/8x9N9d3PLRQT65vndo8er6ynpgzUR2/9qhrVEuXubujOW3fp1Xd7OrxPA3ofNl9fjPHRxq052wig7CsLcViSHnn1uKrVSpOvv2299vY4ulJKkkWrFlY2l0dPPK3B9+S8cOzhKd046rwupVj0xdSs8+DGZUGKj3FXYOWMom0cTmfG4VV2xuGnVkj6Xxxuc0pbduSMcXv2V9Oe/dXy3W5mppsWLWusjAw3PfXo35KkTu1OKjQkUecv2P8/dOctu1SvTqwk6f3POuuu23bZvW5FU2FHx83IyNDu3bs1Z84cPfroo+ratav8/PxksVhksVg0cuTIkm5imRJSLT1H4CuqoOAM3fdkVL6BL1uNOqm6/t7LV5g2Lg3Ks+xtD57LNwHNZrFIoyeeMpcvnPHUoV15JxAoP3btqaHFyxvlGfis/b0xXPsOhJrLndvnfgf88y875puAZktI9Nb8BS3N5U7tHL+j7uaWqX49j5jLy1bXt2vfAFcgDjtXWYjDklS3yaUcCagjTkV661LK5fNan5sv5lu+7y2X38/MsCjqGI8nlCa79tTQ4hWFiMMHreJwB8fj5vI19ZVyKetOqZub1LB+/p8na3Vrx+iOobslSUtX1de2XWEOt6c8q7B3QocNG6Yff/yxpJsBJ2hxdaK++9/rM8edE0zCG19SQOV0JcRk/YucOeGlxq2TnVI3yo9/9lc1nyGtXi3B8fr2Xb5S6++fpsCAS1nPrBRR+9anVSXk8ud28XK64qL0IA6XH8URh+2VnGibrARUyv+u5pV3PTOdlw+jBPyzr6r5DGn1qo7H4YwMNyUmecnHOyt2+vrYd2HHYjH0+ENr5emZqbh4L30y52qH21LeVdhL4hkZtiehkJAQNW7cuIRaA0dYd5PNcGKPGut6M+mpg1xYP9vp5mbkU9Le+myXHa2zv1VX3MMRwTpyNMSh+gBnIg6XH8UVh+1RrZZtknB0v0++5a3f9/DMZITcMs7ZcTgoMEWVgy73mrN3OrObrtun5ldlJcNf/KeDYuPy/xyiAt8J7dSpk5o1a6YOHTqoQ4cOql+/vubMmaNRo0aVdNNQSBH7Lv+jVw1zTlek6CgPxV+8/O9hb70Jse5a9UslHd3vq8R4N/kFZKpKjTQ165Co+s1SeK60nKkffrmbzrnzhX/uKr/6Ui65Kzau6HdB/XxT1a3T5blo/+IuKEoZ4nD5URxx2F5Vw9LUqGWSDu3OGrxm7hs1NGVORK7Tq2WkS7Om1TSX+9920aldg+F69etaxeFoR+Owofvv3SJ396xk9sw5f+0/VKXAtaqFJmjUndskSTv/qa4/lzVysB0VQ4VNQidOnFjSTYATZGZKS7+/fHen3TUFT49ij7/mX64zoFLWhNz2WLeokjmg0ZVqNUjRsIfPatCdF0hGy4GqoQlq2yrKXN62s2Y+pe0zsM/lO5fbd9VQXqPt2qNnt6Py8c66JZGebtHy1fUdbR7gVMTh8qG44nBhPDDllCbe2UDpaW7auLSSHr22ie4cd0aNWycrpGqaLpz11P7tfpo3vboi9mY9o9+iU4IemMxo9mVZ1dAEtW3pWBx2c8tU5UopatbknG4evE9tWpyRJKWluen9zzrb9WzqY/9eL1/fdKWmuWn6p13kSOyuSCpsEory4Zc5oTp+KOsKrJuboSF2TqWSn+goD82fUd1cvu5u22Hei+rkER+9OyFca/+spImfHJWPH1dfy7IHR262uVq6frN9U6bkpXOHE+rQ9vKIkr/82dSh+qxHxd20rZZi4hhcC4DzFUccLqw23RL0ytdH9PK/6yn+oocO/+Onl/+V+4W3kOppuvbOaN39eJQ8PHMtgjLiwRFFi8OzP/hJtWrmfbEk+oKvpr13jXbtqVFgXf16HtbV7bIGs/z2p5Y6fir3GxHIiSQUZVbkfh/NfvXyVa9Bd0ar3lUFj36bn4wM6Y2xdZWUkNWPJ7hqmoY/crbA9arVStU1Q2LU7pp41WuaokpV0pWZIZ0/7aVtawL088yqZpDesKSSXn24ribPiijykPQoWQN6H9Y1XY+Zy7O+apfrvGP2CqmcpHEPrjOXt2yvqU3bij6VSo3q8WrR9PLnlq64AIpDccThomrbI0FfbtijHz+tqv9Or6GM9Jx3o9zcDXXuH6teN8WQgJZxA3pdEYe/diwOZ/tjSSN9PPtqpVwq+ANSKShFD47cLEk6fjJI3/zYyuHtVyQkoSiTEmLd9eLo+kpOzDrh1GqQon9POVXAWgWb+XKYtq8JlJQ10tmE6ccKnD+s27Wx6n/7hVwTytoNL6l2w0u69s4Lev/p2vrr26xnC9b/VUnLfwpWv1vtH/obpUPjhuc19oH15vKy1fW0fE2DItfn4ZGh559caY5iGxvnrTdndHeojQN6HTY/j3HxXtqwxbG7tABwpeKKw0UVdcxLn70YprV/VpJhWFS9dqqatk+Uf1CGYi94aM8mf10856k/vg7VonlVdPcTUbrniTMl1l4UnaNxeOmqBqr0v8GHvDwzVCUkWU0bn1NQYKqu639Indqf1BdftdfSVflfwH1o1EZVCsoa2Or9z7o4JQmuSEhCnejYsWM6duxYwQVzsXPnTie3pvxKTbFoyqj6OhWRNWiLX2CGJn0W6fDgAr/MraIfPr08RcY9T0SpY++Cn20paDh4SfL0MvT428d1KtJbuzdkjbQ2/8NqJKFlTI1q8Xrp2eXy/t+zlocjg/X+p10cqNHQk4/+rRZNz0nKegbl1Xev0YWLfg7V2a/X5blBl6+pr3QCIyoI4rBrFFccLqq9W/z03N0NlRjnLv+gDI19/bh63RhjO2pvuvTr3FB9PjVMaalu+s9bNeXlbWjYmIJ7O6H0qFEtXi89c0Uc/qxwcfir79rk+J2HR4b69zyiB0ZsVpWQZD099m/VqJaor79vnWsdndqfUN9rIiVl9Tba8U/BXXdhiyTUiWbNmqUXX3yxpJtRrmWkS688WE+71mclcl4+mXpxzhE1aO5Y958VCyrro+cu3y0aMuK87hnv3Cukbm5Zie0zw7NGTYvc56tzpzxdPpIgiiakcpJefWGJqgRn3bE8FRWg56b2U1Jy0efEe+T+jerTI1KSlJFh0ZszumvrTscmt27Z7KzCalyeK42uuKhIiMPFr7jicFHFx7jrpfvrKzEu62LbCzMj1LZ7zvki3T2km/7vvLx8MvXek+GSpC/frKE+Qy8Sh8uIkMpJevX5K+Lwy47F4Wzp6e76c1ljRRwL1jtT/5SnZ6buHbZd23bV0J791WzK+vqkaey/su7ExsR667MvOzi8/YqIJ9JQZmRmSm+NC9f6v7Ie+nb3MPTcp5Fq3TXRoXrXLw7Sm4+FKzMz65Jpn6EXNOaVEw63NzetuiTIw/PyleJjB4s+BQdcJzAgRa9NXmIOZBB9wVfPvDhAF2KKfsdy9N1bdeN1+83lGV900oo1jo9gaz0gUeTxSjp4ONThOgFAKr447Ijfv6qiC2eynt/r0Csu1wTU2rV3XVCtBlkJc1qqm5b9GFzsbYTjAgNS9NoLV8ThlxyLw7nZfyhUS1Zmde11c5NuHbInR5kRd2xXtapZsyZ8/mVHxcUzJ2hRcCcUZcb7T9fWsh+zhoF3czP05PSj6jIgzqE6t68J0Cv/rqf0tKzrMV0GxurJ6ceKbcAgD08pKCRDF85kbSDuAv+CpZ2fb6pefX6p6oXHSMq66vn0iwMUdTawyHXeeetO3XHLbnP58y/b67e/rnK0qfLySlfPrkfN5cXcBQXgRMURhx21ecXlc3GbAhJQSbJYskbTPXkkK3E4uNO5SQycL9c4/JJjcTg/W3fW1HX9D0mSmv/vcRlrDetfkJR1UWbIoP0aMmh/jjLZwmvHmq+v7XdIHdtefm76sYmDndXkMqncfAPet2+f9u3bl+f7TZs2VdOmjk15UJDRo0erf//+RVp3586dGjNmjJNbVH58MjlMf3x9+Y7O2NePq8/QGIfq/GejvyaPrK/UlKyEsG2PeD33aaRTpmPJT0rS5QyXaVpKNx/vNL383FI1aZQ15UBCoqeee7m/jp2oXOQ6h16/R6Pu2m4uf/1dK333c0sHW5qle6dj8vfP6laWkWHR0pVFHzAJKCzicPlWHHHYGaKjLo9iGhScbtc6QSGXyyXG0SmwNPPxTtPLE5eqSUOrOPyKY3G4IAmJl3upBQVcyrOcm5vUrMl5u+utWiVJVavYN+98RVBuktBvvvkm3+dAJk+erClTphRrG8LDwxUeHl6s26iIZr9WQz99frk//r9fPKnr7r7gUJ0Hdvhq0r0NlJKU9QxJ844JenFOhLy8DYfqLcjpo15Kir88SEyVGjyHUlp5emboxWeWq2WzrKugKSkemvRKPx08UqXIdQ4ecEAPjd5sLv/4azPN/aadw23N1r/35QGJtu6o6fRuSkB+iMPlV3HEYWfx8rkct+Nj7PtaG3fxcjl/OwYXRMnINQ5PcywO2yOkcrL5Oj7B8edNkbtyk4SifPrv9Or65v3LI47d9+Rp3fKvnF0jCiNir48m3tXQTAYbtUrSy18dccldyUXfhJiv/YMy1LBFcj6lUVLc3TP1wpMr1K51lCQpNdVNk1/vnWNwgsLo1/OIzZDyfyxppE9md3S4rdlCgpPUvvVpc3nxCrriAnBcccRhZ6oWlqqIPb6SpB1rAwoc7dYwpJ1rA8zlsHqpxdo+FI27e6ZemLBC7Vo5Lw7bq3PHy+OCHDtZKcf7T04eZHddb764SG1aZA10+Z/5rfWf+W0dbl95UW76IEyZMkWGYeT5U9xXX+F8P30RqrmvX54E+/aHz+juxx0bsfbEYW89e0dDxf/vKmh4k2RN++9h+QcVLQFNTrT/X+ifTX42U8D0uulisXf9ReG5uWXq2XGr1bnDSUlSerpFL7/dS9scGLW2e+ejmvDI3+azxstW19N7n3SVlHMy9aLq1/OI3N2z7ggkJHrq743cDYJrEYfLn+KIw87WruflqdS2rAjUrvX++Zb/69sQnTh8eSCZjr1L9plW5JRrHH6nl7btKnwcdnPLlJ+f/Rcarm53Qj06Xx5bYc36uoXeJuxTbpJQlC+L5oXo08m1zOUbRp7T/ZNO57NGwc6e8NQzwxvq4rms50fC6l/Sa98eVqUqRe+Ks/rXynp0cGMt/i44z+dKUlMsWvBFqJ4d3sh8/jSgUrrueSKqyNtFcTH0xMPr1LNbVgDKyLDojfd7aP3mOkWusWPbk3r28dVmgrh2Yx298X4PGYbzElBJGmDVFXfl3/WUlsbcoACKrjjicHEYOOyCAv/3LKhhWDRldH2t/jXn3auMdGnh7FB98Ozl6diuapdYoiP7IjeGnnhonTnIXkaGRW98UPQ47OuTri8//FH3Dd+u2mGxeZYL8L+ku27dqclPrjAvGJ88Hag/ljQu0nZRMO7DwGkm3dPAZoAASbp47vJH7MAOXz3UP+cIoC9/dVhValweJCBir4/ee7KO+SXdxy9DMqQZE2vlWDc3Q+8/p1oNcl71mvpAPZ07dblvf3ijFM2bXt2uOpt1SFK/Wy/m+t6B7f566zF/uXsYqtMoRXUaXVJApXRlZlh0PspTe7f42zwH6u2TqSmzI1Slun0DKMB1bhi0XwP7XJ7i5PSZALVodlYtmtk3mfmHX3S2WQ4KTNELT62Q1/+m5cnIsCgm1lsPjd5kV31LVzbQvoNVCyzXuEG0OWqgRFdcoKIq7XE4uw3vjs/ZUyP6zOV2fvlmDf30ue25r0mbJD3+9vEc6/kHZeqJt49p6r/qKzPDooQYD738QH1Vr3NJzdonyS8oQ3EXPLRnk78unL18bAKD0/Xk9GN27Q9cJ9c43PSsWjS1Mw7P7Jzjd0GBqbrn9p265/adOh/tq4hjwYqJ9VHKJQ/5+qQrrEacGjW4YMZqSYq+6KsXXuujtHQu6BYXu5JQd/fi/wNYLBalp/OlvCw7dsBHZ07k/QB3SpK7jvzvuQ1raWm2dxDjLnqYc3Zmr/fL3IK/iGe7ZkhsrsEv9rxtYF6/OOeV0rykJF3IMwnNlpFuUeQ+X0Xuy7mP2a5ql6gJ7x1TeOO8R1tDyalcyXay9dph8aodlvfQ61e6Mgn19UmXj/flO+3u7oYGDzhkd30HDlexKwm1nhv0+MkglzwzA9ciDsMepT0OS1JyYu5tsHbulJfOnbL9XUA+Awh1uzZOL809onfGh5tzhp457q0zx3Ofi7thiyQ9/eFR1WlELC5tHI7DVyShhpHVndfDI6s3UmiVZIVWyX88jtXrw/XRrE6KvsDgfsXJriTUMAxZLBYZRvGOHOpKERERmjlzps3vdu7cab7etm2bJk2aZPN+37591bdvX5e0D2VD75svqlaDFO3Z7K99W/11OtJLsRc8FH/RQ5mG5B+YoRrhqWraPknXXB+jlp3p9gPncnfPVO8eEebyEqZlKZeIw1mIw8jL1X3jNWftHq36pbI2LA3S4d1+ijnnoZRkN/kFZCikepquapukHtfHqlO/uGKbDxylS1Kyl24bNVztWp9Wi6vOqVH9C6pRPV6Vgy7J0zNDKSkeik/00tHjlbXvYKiWr6mvU1FBJd3sCsFi2BHR3Fzwn2qxWJSR4bphslesWKE+ffoUap3iHF5+zZo1uuaaa8zllQtqqUfn/K8UAiVh4K0jSroJQK5i4o5qy+4vzOXVq1erR48eJdgi5yEOZyEOA9LA24jDKJ1i4o5qyy774rBdd0IzM4t/6goAAJA74jAAoDypsAMT9e7du1x1awIAoCwhDgNAxUWPeAAAAACAy5CEAgAAAABcpli74yYnJ+vixYtKT09XeHjOOaEAAEDxIQ4DAEojpyahhmHo+++/19dff601a9bo4sWseRVzm3vs/Pnzmj9/viSpcePGGjBggDObAgBAhUMcBgCUBU5LQvfv36/hw4dr165dklTgYANVqlTRe++9p8OHD6tGjRo6fvy4S4agBwCgPCIOAwDKCqdEm71796pr167atWuXDMOQYRjy9/eXv79/nutYLBY99NBDMgxDUVFRWrFihTOaAgBAhUMcBgCUJQ4noRkZGbrlllsUExMjwzDUt29frVu3TvHx8Ro5cmS+6w4bNsx8/ddffznaFAAAKhziMACgrHG4O+7XX3+t/fv3y2KxaOjQoZo/f77d3Xlq1aql+vXrKzIyUps3b3a0KQAAVDjEYQBAWePwndCffvpJkuTj46OPP/640M+TtGzZUoZh6ODBg442BQCACoc4DAAoaxxOQrds2SKLxaIePXqoatWqhV4/NDRUkhQdHe1oUwAAqHCIwwCAssbhJPTcuXOSpHr16hVpfU9PT0nKMXQ8AAAoGHEYAFDWOJyEent7S5LS0tKKtH528AwODna0KQAAVDjEYQBAWeNwElq9enVJKvKzJBs3bpTFYlGdOnUcbQoAABUOcRgAUNY4nIR27dpVhmFo06ZNOnPmTKHWXbx4sU6ePClJ6tmzp6NNAQCgwiEOAwDKGoeT0JtvvllSVjegZ5991u714uPjNXbsWHP59ttvd7QpAABUOMRhAEBZ45QktE2bNjIMQ3PnztUTTzyh1NTUfNfZtWuXevbsac5r1q9fP3Xu3NnRpgAAUOEQhwEAZY2HMyqZO3euevbsqfj4eE2fPl3ffvutbrnlFu3YscMsM336dEVFRenvv//W2rVrZRiGpKyh4WfOnOmMZgAAUCERhwEAZYlTktDWrVtr4cKFGjZsmM6ePauoqCh99NFHkiSLxSJJeuKJJ8zy2YGvZs2a+vnnnxkMAQAABxCHAQBlicPdcbP17NlTO3bs0KhRo+Tp6SnDMPL88fDw0MiRI7V582Z17NjRWU0AAKDCIg4DAMoKp9wJzVa9enXNnDlTr7/+upYsWaJ169bp1KlTio2Nlb+/v6pXr67OnTtr0KBBCgsLc+amAQCo8IjDAICywKlJaLbQ0FDdcccduuOOO4qjegAAkA/iMACgNHNad1wAAAAAAApCEgoAAAAAcJli6Y4rSQcOHNC2bdt07tw5xcfHKzAwUKGhoWrfvr2aNGlSXJsFAAAiDgMASi+nJqGxsbF69913NXPmTJ06dSrPcjVr1tT999+vcePGqXLlys5sAgAAFRZxGABQFjitO+7ixYvVvHlzTZ06VSdPnsx3aPhTp05p6tSpat68uRYtWuSsJgAAUGERhwEAZYVT7oT+8ssvuv3225WWlmZOgO3u7q6rrrpK4eHh8vf3V2Jioo4fP659+/YpIyNDkhQVFaUbb7xR3333nW688UZnNAUAgAqHOAwAKEscTkLPnDmjkSNHKjU1VVLWsPAvvPCC7rnnnly7+MTGxuqrr77S1KlTde7cOaWlpWnUqFHas2ePqlev7mhzAACoUIjDAICyxuHuuDNmzNDFixdlsVjUsmVL7dq1S4888kiez5hUqlRJY8aM0Y4dO9SiRQtJUkxMjGbMmOFoUwAAqHCIwwCAssbhJHThwoVZFbm5af78+XZfRa1evbq+/fZbubm52dQDAADsRxwGAJQ1DiehkZGRslgs6t69u5o2bVqodZs1a6YePXrIMAxFRkY62hQAACoc4jAAoKxx2ui4RZ1zjLnKAABwHHEYAFBWOJyE1q5dW5KUkJBQpPWz18uuBwAA2I84DAAoaxxOQgcOHCjDMLRmzZpCr5u9nsViUf/+/R1tCgAAFQ5xGABQ1jichD744IPy9vbWyZMnNX369EKtO2PGDJ04cUJeXl568MEHHW0KAAAVDnEYAFDWOJyEXnXVVXr//fdlGIYmTJigt956S5mZmfmuYxiG3nnnHT3++OOSpPfee0/NmjVztCkAAFQ4xGEAQFnjYU+hY8eO5fv+oEGD9N577+mpp57S008/rU8++UR33XWXunbtqvDwcPn5+SkpKUnHjh3T+vXrNW/ePB0+fFheXl564403dN111+nYsWMKDw93yk4BAFCeEIcBAOWJXUlovXr1ZLFY7KrQMAwdOXJEr7zySr5lJCk1NVXjxo3TuHHjZLFYlJ6ebtc2AACoSIjDAIDyxK4kNFt20MqLxWIxg2R+Za0DaUF1AgCALMRhAEB5YFcSGh4ebvcVWAAA4FzEYQBAeWJXEhoZGVnMzQAAAHkhDgMAyhOHR8cFAAAAAMBeJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZQo1RUthxcbGKj4+XpmZmXaVZ5JsAACchzgMACiNnJqEHj16VJ988omWLFmiXbt2KS0tze51mSQbAADHEIcBAGWB05LQt956S5MmTTIDHpNfAwDgOsRhAEBZ4ZQk9M0339TTTz9tLgcEBMhisSg+Pl4Wi0Xh4eGKj4/XxYsXzaBosVjk4+OjatWqOaMJAABUWMRhAEBZ4vDARMePH9ekSZMkZQW9b7/9VjExMbrvvvvMMhERETp//rxiYmL022+/6frrr5dhGEpLS9O///1vRUREKCIiwtGmAABQ4RCHAQBljcNJ6Keffqq0tDRZLBbNmDFDt99+u9zccq82MDBQ1113nX755RfNmzdPFotFzz33nF566SVHmwEAQIVEHAYAlDUOJ6HLly+XJIWGhuree++1e73hw4frnXfekWEYmjp1qnbs2OFoUwAAqHCIwwCAssbhJPTw4cOyWCzq3LmzLBZLrmXyGm3v4YcfVs2aNZWZmalZs2Y52hQAACoc4jAAoKxxOAm9ePGiJKlmzZo2v/f29jZfJyUl5bquxWLRNddcI8MwtGzZMkebAgBAhUMcBgCUNQ4noV5eXpKU4+prUFCQ+frEiRN5rh8QECBJOnnypKNNAQCgwiEOAwDKGoeT0Oyh3WNjY21+X69ePfP11q1b81z/yJEjkqTk5GRHmwIAQIVDHAYAlDUOJ6HNmzeXYRg6dOiQze/btWtnvp43b16u6x44cEB///23LBaLwsLCHG0KAAAVDnEYAFDWOJyEdu/eXZL0zz//6NKlS+bvW7VqpSZNmsgwDP3555965ZVXlJGRYb4fGRmpu+66S2lpaZKkPn36ONoUAAAqHOIwAKCscTgJHThwoCTp0qVLWrFihc17zz77rPn6hRdeULVq1dS9e3e1a9dOjRs31rZt2yRJHh4eevzxxx1tCgAAFQ5xGABQ1jichLZv314dO3ZUtWrV9Msvv9i8N2LECI0cOVKGYcgwDF28eFHr16/Xzp07lZGRIcMw5Obmpg8++EAtWrRwtCkAAFQ4xGEAQFnj4YxKNm7cmOd7s2bNUpcuXfT222/r4MGDMgxDUtYofl26dNHUqVPVt29fZzQDAIAKiTgMAChLnJKEFuSBBx7QAw88oBMnTujUqVNyc3NT/fr1VaVKFVdsHgCACo04DAAoTVyShGarXbu2ateu7cpNAgCA/yEOAwBKA4efCQUAAAAAwF4koQAAAAAAlyEJBQAAAAC4jF3PhI4ePbq42yGLxaKZM2cW+3YAAChriMMAgPLEriR0zpw5slgsxd0Wgh8AALkgDgMAyhO7R8fNnlesuLgiuAIAUFYRhwEA5YVdSejs2bOLux0AACAPxGEAQHliVxI6YsSI4m4HAADIA3EYAFCe2N0dF641/ubGqmwJLelmADl41r1Q0k0AcuWRElfSTUA5QhxGaUUcRmlVmDjMFC0AAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALuNRnJUnJyfr4sWLSk9PV3h4eHFuCgAAXIE4DAAojZyahBqGoe+//15ff/211qxZo4sXL0qSLBaL0tPTbcqeP39e8+fPlyQ1btxYAwYMcGZTAACocIjDAICywGlJ6P79+zV8+HDt2rVLUlYgzE+VKlX03nvv6fDhw6pRo4aOHz8uNzd6BwMAUBTEYQBAWeGUaLN371517dpVu3btkmEYMgxD/v7+8vf3z3Mdi8Wihx56SIZhKCoqSitWrHBGUwAAqHCIwwCAssThJDQjI0O33HKLYmJiZBiG+vbtq3Xr1ik+Pl4jR47Md91hw4aZr//66y9HmwIAQIVDHAYAlDUOd8f9+uuvtX//flksFg0dOlTz58+3uztPrVq1VL9+fUVGRmrz5s2ONgUAgAqHOAwAKGscvhP6008/SZJ8fHz08ccfF/p5kpYtW8owDB08eNDRpgAAUOEQhwEAZY3DSeiWLVtksVjUo0cPVa1atdDrh4aGSpKio6MdbQoAABUOcRgAUNY4nISeO3dOklSvXr0ire/p6SlJOYaOBwAABSMOAwDKGoeTUG9vb0lSWlpakdbPDp7BwcGONgUAgAqHOAwAKGscTkKrV68uSUV+lmTjxo2yWCyqU6eOo00BAKDCIQ4DAMoah5PQrl27yjAMbdq0SWfOnCnUuosXL9bJkyclST179nS0KQAAVDjEYQBAWeNwEnrzzTdLyuoG9Oyzz9q9Xnx8vMaOHWsu33777Y42BQCACoc4DAAoa5yShLZp00aGYWju3Ll64oknlJqamu86u3btUs+ePc15zfr166fOnTs72hQAACoc4jAAoKzxcEYlc+fOVc+ePRUfH6/p06fr22+/1S233KIdO3aYZaZPn66oqCj9/fffWrt2rQzDkJQ1NPzMmTOd0QwAACok4jAAoCxxShLaunVrLVy4UMOGDdPZs2cVFRWljz76SJJksVgkSU888YRZPjvw1axZUz///DODIQAA4ADiMACgLHG4O262nj17aseOHRo1apQ8PT1lGEaePx4eHho5cqQ2b96sjh07OqsJAABUWMRhAEBZ4ZQ7odmqV6+umTNn6vXXX9eSJUu0bt06nTp1SrGxsfL391f16tXVuXNnDRo0SGFhYc7cNAAAFR5xGABQFjg1Cc0WGhqqO+64Q3fccUdxVA8AAPJBHAYAlGZO644LAAAAAEBBSEIBAAAAAC5DEgoAAAAAcBmHnwn98ssvndEOSdJ9993ntLoAAKgIiMMAgLLG4SR05MiR5hxkjrBYLAQ/AAAKiTgMAChrnDI6bvak1/ayWCyFXgcAAOSOOAwAKEscTkJHjBhhV7nMzEzFxsZq165dioiIkCT5+Pjo9ttvl5sbj6YCAFAUxGEAQFnjcBI6e/bsQq+zefNmPfbYY1q3bp2ioqL03XffKSgoyNGmAABQ4RCHAQBlTYlc+uzYsaNWrVqlgQMHasmSJTyDAgCACxGHAQAlqcT637i7u+uLL76Qp6enfvnlF/34448l1RQAACoc4jAAoKSU6EMgtWvXVvfu3WUYRpG6EwEAgKIjDgMASkKJj0TQsGFDSdKOHTtKuCUAAFQ8xGEAgKuVeBKakpIiSTp79mwJtwQAgIqHOAwAcLUSTUIzMzO1atUqSVKlSpVKsikAAFQ4xGEAQEko0SR00qRJOnbsmCwWi9q3b1+STQEAoMIhDgMASoLD84QeO3bM7rLp6emKjo7W9u3bNXfuXK1bt858z97JtgEAwGXEYQBAWeNwElqvXj1ZLBaH6hg8eLDuuOMOR5sCAECFQxwGAJQ1TuuOaxhGoX8sFosefvhhff/9985qBgAAFRJxGABQVjh8JzQ8PNzuK7Cenp4KCgpSvXr11LlzZw0fPlzh4eGONgEAgAqLOAwAKGscTkIjIyOd0AwAAFAUxGEAQFlT4vOEAgAAAAAqDofvhO7cudN83aJFC7m7uztaJQAAsBNxGABQ1jichLZt21YWi0V169bVkSNHnNEmAABgJ+IwAKCscbg7rqenpySpS5cuDjcGAAAUDnEYAFDWOJyE1qhRQ5IUEBDgcGMAAEDhEIcBAGWNw0lo06ZNZRiGjh496oz2AACAQiAOAwDKGoeT0GHDhkmS1qxZo+joaIcbBAAA7EccBgCUNQ4noXfffbeaN2+ulJQUjRkzxhltAgAAdiIOAwDKGoeTUB8fH33//feqU6eOvvvuOw0ePFgHDhxwRtsAAEABiMMAgLLG7ilaXnrpJUlSp06ddO211+b4/Y033qhPPvlEixYtUrNmzdS6dWt16NBBVatWla+vr13beOGFFwrTdgAAKgziMACgvLAYhmHYU9DNzU0Wi0VjxozR+++/n+P31gzDyPE7e2RkZBR6nfJizZo1uuaaa8zljuqtypbQEmwRkDuPunVKuglAri6mnNSGqPnm8urVq9WjR48SbJFzEYeLF3EYZQVxGKVVYeKw3XdC85NbHmtnbmsqSrAEAADEYQBA2eJwEjp58mRntAMAABQBcRgAUNaQhAIAUIYRhwEAZY3Do+MCAAAAAGAvklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMoUemOinn37S7t27nd4Qi8WipUuXOr1elG3Va6eqfc94teqaoPpNU1S1Vpp8/TOUnOCuc6c9tXeLn5b/FKxd6wPsqm/RqR2F2n5GujQ4vE1Rmo4yzM3NUHj9eDVpHqNGTWPVpFmM6jWKk6dn1pQXO7eG6Nkx3eyqy8s7Q1c1v6imrWJUr2GcaocnKrRasvz802WxSIkJHjp90l97dwVr+aJaOnKgkl31enhkqmW7aLXpeF6Nm8aqdt0EBVVOlSTFx3op8nCgtm8K1eLf6ighzqtoBwKlEnEYrkQcRkkgDpd/hU5CT506pVOnTjm1EUWdVBvlV8OWSRr72kk1bZ+U6/uBwRkKDM5Qg+Ypuv7eC9rxt7/eHBeucyf5J4djuvSM0pNTtsnHN8Mp9Q25NVL/9+jePN+vHJKqyiGpatbqom6564hWLg7TR2+1zDdgjRqzR9feeFwBQWm5vu9dLUWh1VLUses53X3/Ac2a0Uy//1TP0V1BKUEchisQh1FSiMMVQ6GT0MJOfg0URe2Gl3IEvuOHvXV0n49iL7groFKGmndMUtWwrH/+Nt0T9d7Cgxo/tJGijnnbtY2Fs6sUWCYjgy9lFU1AQJrTAt+VUpLddTwyQKdP+ikxwVPu7oaqVE1R05YX5R+QLknqNeCUwuvH68l/d1Nykmeu9XS+5oxN4EtOctf+PZUVfc5H6WluCquTqKYtL8rT05CvX4bGPLVbVWska+7HzYplv+BaxGG4AnEYJYU4XDEUOgnt1KmTrrvuuuJoC5DDySNe+nNeFS39IVjRUbYnAovF0IDhFzTm5VPy8ctUaM10PT3jmB6/sZGkgoPWh8/VLqZWozy4GO2tA3sr6eDeyjqwp7Ladzmnm4dHFLqeU8f9NfeTq7R1fVUdORSkzIycj+J7emXo5uERuvff++TuLtVvFK8RD+3TJ2+3yrPe9HSL/l5eU3/9Ukc7t1bJUW/V6ska99wOtb36vCRp2H2HtXtbFW1ZX63Q+4DShTgMVyIOo6QQh8u3IiWhTIyN4nbhjKfeGldHS78PVmZm7oHMMCz665sqSojx0ORZkZKk5h2T1KFXvLasDHJha1GebFlfVSNv7qdzZ3xtfn9Vi4tFqm/96hpav7pGvmXSUt313X8aycMzU/f864Akqd91JzXzg+ZKS3XPUX7V4jAt+6O2ok7551nnuTO+mjz+ar3xyVpd1TxWknTn6AMEv3KAOAxXIA6jpBCHK4YKPTpuRkaGdu/erTlz5ujRRx9V165d5efnJ4vFIovFopEjR5Z0EyusXesDtHh+SJ6Bz9raPytp31Y/c7lT//jibBrKuYsXfHIEPlf565c65ms//3SF1U7Mtdx/Z16Vb+DLlp7mrq8/v8pcvqpFjAKDUh1vKOAkxOHSiziMkkIcrhgKfSe0PBk2bJh+/PHHkm4GnOCfTX7msyvV6/DPjbIpNsZ2EARfP8efidmzM9h87eYmVauRrHhG6UMpQRwuP4jDKA+Iw65T4e+EWgsJCVHjxo1LqDVwiHH5Sq2bG4N2oGwKr59gs3zmtDOuBNvexXBz5/8DpQdxuBwhDqMcIA67ToW+E9qpUyc1a9ZMHTp0UIcOHVS/fn3NmTNHo0aNKummoZDqNUs2X58/Zd/VpZadE9S0XZIqV01XZoYUd8FDR/b46p9NfrqUnLP/P1CcPDwyNfKhfebynp3Buhjt43C99RrG2SyfP+N4nYCzEIfLD+IwyjrisGtV6CR04sSJJd0EOEHVWqlq2/3ylautq+2bMPvtnw7n+vuUJDct+iZYX79bXbHRuQ/NDTiDh0emgkNT1LLNBQ2964gaNskKVEmJHvrk7ZZO2Ub/wSfM15GHA3XxAsEPpQdxuHwgDqOsIg6XnAqdhKJ8+PfkU3L/3yf5zAlPbVjs2Ih8Pn6Zuml0tHoMjtVL99fTvq0FP3gO2Gvh6t/k7pF3V5wTR/01bWIHHT3i+MiSDRrHasCQ4+bybz/UdbhOALgScRhlCXG4dChUEsoE2Sht+t9+QdcMiTWXZ79aU2mpeT/qnJpi0bq/grRxaZAO7vDT2ZOeSr3kpsDK6WrSJlkDhl1Qj+tj5eYmVamRrqlfRmjcjY118oh9E28DRZWRbtH3XzXUV180yXUOs8Ly9knX+MnbzUB79EiAFi0Md7helCziMEob4jDKC+Kwa9mdhEZEZE0OGxTEvE8oHRq3TtLY1y53cVj+U2Ut/yk4nzWkuzo0V/zFnB/7mPOe2rjUUxuXBqlz/zg992mkvH0NBYVk6NFXT+iZ4Q2d3n5UTL/+UNcclMDHJ0NVq6eoSfMY+fmna/jIQ+rR75Q+faelg/OIGXp80g7Va5g1TULqJTe98UJ7ZTghqKLkEIdR2hCHURYRh0sHu5PQunW5fVyQY8eO6dixY0Vad+fOnU5uTflWvc4lvTg3Qt6+WSeRI//46P2naxe4Xm6B70oblgTp4+dradxbWYG13TUJatQqSYd2+RWwJlCwz97L+YyJt0+6htwaqXv+dUC16iRp8lsbNX1aGy39vU4uNRRs1MP7dE2/0+byh2+0UuRhEpeyjjhcMOKw6xCHUVYRh0sHngl1olmzZunFF18s6WaUeyHV0vTaN0dUpXq6JOlUpJeeu7uBkhKcN5Len/NCdOe4M6peO02SdHXfeIIfis2lFA/98HUjnTrhr0mvbZG7uzTmqV36Z3uIXZNhW7v17kO67d7Lg33MmtFUS4oYRIGyhjjsGsRhlDfEYdfjnjDKlMDgdL36zRGF1c+aCDs6ykPPDG+gC2edO3qeYVi04+/Lo/uFN05xav1AbtatrKntm0IlSd7embr+1qOFWv/am49q9COXh5ef/2VD/fB1I6e2EUDFRhxGeUYcdh2SUJQZfgEZmvbfI6rXNCsQxUS765nhDXXmePEMVnDhzOWAGhSSXizbAK60bWOo+bpZqwt2r9dn0AmNeXKXufzbD3U19+NmTm0bgIqNOIyKgDjsGnTHdaLRo0erf//+RVp3586dGjNmjJNbVH54+2Zo6ldH1KRN1mTYCbFueu6uBjp2sPjmWvL2yzRfpyQxaTZcIyHe6ktXpTS71unW+7Qen7RDbv+7rLj0j1r66C3nzG8GlCXE4eJDHEZFQRx2DZJQJwoPD1d4OEMvO5und6ZenBOplp2SJGVNYv38fQ2K/dmQRi2TzdcXzvCvAtcIrnLJfB0fV3D3tqu7ndFTL201h4D/e3kNvfdyW0mWYmohUHoRh4sHcRgVCXHYNeiOi1LN3cPQ859Hqt01CZKy5hebMqqe9mwq3omr6zRKUfOOiebyjrUB+ZQGnKdT9zPm6+OR+X/uWnc4r2enbZGnZ1bg27S2qt54ob0yMwl8AJyDOIyKhjjsGiShKLXc3Aw98+FRde6fNcdSepr0yoN1tW11YJHq8/HLsKuct2+mxr97XO7/u+gaE+2uzcuLtk1UbN4+6fL0su9zJ0mDh0aqSfPLk77/vaJmnmWbtbqgF97YJG/vrO5qO7eGaNrEjkpP57QOwDmIwyjriMOlF0cJpZShx98+rp43ZJ0IMjKkN8aGa/1flYpc45cb9+q+J6NUp1HeI+w1vzpR7y08qGYdki6v90YNnkVBkdSqk6gvvluuW+4+rNBqyXmWCw5J0b8e+0cPTdht/m73thBt+rt6ruUbNInVlLc3yvd/X+j27a6sFyd0UuolPqcAnIU4jLKPOFx60cEepdKQEdEaOPyiuXw60kstOyWqZafEfNa67MPnck6YXSkkQ3c/fkZ3P35G5097KGKvry6e81DaJTcFVk5Xo9bJCquXarPOwtlV9Nt/QnPUhfJtytsbVCX0ks3vrJ8Radw0Vh/MXZVjvcnjO+nCedtBOkKrpej/Htmr/3tkr6JO+erokUDFxXgpLc1Nfv7pql03QfUbxpvPkkjS8aP+eu359nm2b+q7GxQQeHmkyNMn/TTy4b127dvmtdW0eV3uQRUAshGHUZKIw+UfSShKpcqhtkOx126YqtoNo+1eP7fgZy20ZrpCa8bn+X78RXd98UpN/fnfKnZvE+VHeP0EVa+Z9xVTX78MNWgSl+P3Hh6ZNsvp6W7KyJDc/3dhtEZYsmqE5V1vRoa0+Jdwzf6oqRLivfIsVznE9ktan0Gn8ix7pbgYL4IfgAIRh1GSiMPlX4VOQiMiIjRz5kyb3+3cudN8vW3bNk2aNMnm/b59+6pv374uaR+ca1T3pmreIVHNOiSpQYtkVQpJV1BIhnz9M5Sc6K6YaA8d2umrrasDtPLnYF1Kprc6HHMsIlD3DBmgdp3Oq1mrC6rfMF41aiUpsFKqPNwNJSd5KC7OU5GHA7V3Z4hWLg5T9Dnfkm424DLE4YqFOAxXIw6XXhbDMIyCi5VPK1asUJ8+fQq1zuTJkzVlyhSnt2XNmjW65pprzOWO6q3KFrqfoPTxqFunpJsA5OpiykltiJpvLq9evVo9evQowRahIMRhoPCIwyitChOHucQEAAAAAHCZCt0dt3fv3qrAN4IBAChRxGEAqJi4EwoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXIYkFAAAAADgMiShAAAAAACXIQkFAAAAALgMSSgAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAl/Eo6QYgS0JCgs1yvGIko2TaAuTHPYVrVyid4lPP2SxfeV4F8kMcRllBHEZpVZg4TBJaShw5csRmeb+2l0xDgIJElXQDAPtceV4F8kMcRplBHEYZkV8c5lIKAAAAAMBlSEIBAAAAAC5Dd9xSYsiQITbLDRo0UEBAQAm1pnzYuXOnxowZYy5/+OGHat26dQm2CLiMz6fzJSQk2HT9ufK8CuSHOOx8nOdQmvH5dL7CxGGS0FIiPDxcDz/8cEk3o1xr3bq1evToUdLNAHLF5xMoWcTh4sd5DqUZn0/XojsuAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKAAAAADAZUhCAQAAAAAuQxIKAAAAAHAZklAAAAAAgMuQhAIAAAAAXMajpBsAFJfw8HBNnjzZZhkoLfh8AijvOM+hNOPzWbIshmEYJd0IAAAAAEDFQHdcAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJRbmVkZGh3bt3a86cOXr00UfVtWtX+fn5yWKxyGKxaOTIkSXdRFQgfB4BVDSc91Ca8HksXZiiBeXWsGHD9OOPP5Z0MwBJfB4BVDyc91Ca8HksXbgTinIrIyPDZjkkJESNGzcuodagouPzCKCi4byH0oTPY+nCnVCUW506dVKzZs3UoUMHdejQQfXr19ecOXM0atSokm4aKiA+jwAqGs57KE34PJYuJKEotyZOnFjSTQBMfB4BVDSc91Ca8HksXeiOCwAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlmCcUZca+ffu0b9++PN9v2rSpmjZt6sIWAQBQcRCHATgLSSjKjG+++UYvvvhinu9PnjxZU6ZMcV2DAACoQIjDAJyF7rgAAAAAAJfhTijKjClTpnCFFQCAEkIcBuAs3AkFAAAAALgMSSgAAAAAwGVIQgEAAAAALsMzoSi3IiIiNHPmTJvf7dy503y9bds2TZo0yeb9vn37qm/fvi5pHyoWPo8AKhrOeyhN+DyWLiShKLeOHj2qV155Jc/3d+7caXPykSQPDw9ONigWfB4BVDSc91Ca8HksXeiOCwAAAABwGYthGEZJNwIAAAAAUDFwJxQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJchCQUAAAAAuAxJKOBkvXv3lsVikcVi0YoVK3ItM3LkSLPMnDlzXNq+smLOnDnmMRo5cqTD9ZXEMY+MjDS3Wa9ePZdssyhWrFhhtrN3794l3RwAcAhx2DmIw65TEeMwSSicyvrEn9tPYGCg6tWrp5tuukkffPCBYmNjS7rJAACUG8RhAGUBSShcKiEhQUePHtXChQs1duxYhYeH68svvyzpZpV71l9AAAAVF3G4ZBCHAVseJd0AlF9XX321OnXqZC4bhqGYmBht2rRJBw8elCTFxcVpxIgRSklJ0QMPPFBSTQUAoNwhDgMorUhCUWwGDx6sKVOm5PreTz/9pFGjRpndgMaOHavBgwerdu3aLmxhyZkzZw7PoAAAihVxOG/EYaBk0R0XJWLo0KH6+uuvzeVLly7po48+KsEWAQBQcRCHAZQkklCUmOuvv15t2rQxl5csWVKCrQEAoGIhDgMoKSShKFHdunUzXx85csTmvSlTppgP8Wd3J0pOTtbMmTM1cOBAhYeHy8vLSxaLRdu3b8+1/qVLl+rBBx9UixYtFBISIm9vb4WFhWnQoEGaMWOGkpOT7W5rZmam5s6dqwEDBqhGjRry8fExRxhcsGBBofa7KMOUr1mzRo899pjatWunatWqydPTU0FBQWrVqpVGjBihefPm2eyP9XDf1vIaMTEyMjLPbR8/flxTp07VNddco7CwMHl7eyskJETt2rXThAkTdODAgULt/9atW/Wvf/1LDRo0kK+vr6pWrapOnTrpjTfe0IULFwpVl7OlpaVp0aJFeuqpp9SnTx+FhYXJx8dHvr6+ql27tq677jq99957SkhIKPI2li5dqjvvvFMNGzY09/+aa67RjBkzdOnSpULVlZiYqI8//lg33HCD6tatKz8/PwUGBqpx48YaPXq0li1bVuR2Aij/iMPEYeIwcbhEGIAT9erVy5BkSDImT55cYPmJEyea5T09PW3emzx5sk1de/bsMVq0aGH+zvpn27ZtNuseO3bM6N27d65lrX/CwsKMVatWFdjO06dPG507d863rqFDhxpxcXE2x2D58uW51jdixAizzOzZs/Pd9vHjx40BAwYUuC+SjM6dO5vrLV++3K51sn8iIiJybDsjI8N4/vnnDR8fn3zX9fDwMCZOnGhkZmYWeCyfe+45w93dPc+6ateubaxbt86YPXu2+bsRI0YUWG9B7Dnmx44dM6pUqWLX8apSpYrx119/5bvNiIgIs3zdunWN1NRU44EHHsi33mbNmhn79++3a5/mz59v1KhRo8C2DhkyxIiJicmzHuvPSq9evezaNoDSiThMHC4IcZg4XBowMBFK1MWLF83XlSpVyrNcdHS0rr32Wh07dkw+Pj7q0aOH6tatq4SEBK1fv96m7N69e9WvXz+dPn1aUtYVx/bt26t58+by9fXVyZMntWrVKsXHx+vUqVMaMGCA/vjjD/Xp0yfXbcfExKhv377au3ev+bv69eura9eu8vb21j///KONGzfqp59+kpubczsX/PPPPxowYIC5L5JUrVo1devWTVWrVlVKSooOHz6sbdu2KTk5WSkpKWa5WrVqacyYMZKkDz/80Px99u+uFBQUZLOckZGh4cOH64cffrCps1OnTqpataoSEhK0YcMGHT58WOnp6Zo2bZrOnTunzz77LM/9mThxol599VVz2c/PT3379lXNmjUVFRWlZcuW6cSJExo8eLDGjRtn30FyosTEREVHR0uSgoOD1aJFC9WtW1cBAQFKTU1VRESE1q9fr5SUFEVHR2vw4MFauXKlzZ2E/Dz99NPm8WndurXatm0rwzC0ZcsW7dmzR1LW57dv375at26d6tSpk2dd7777rsaPHy/DMCRl/f26du2q2rVrKyMjQ//88482b94swzD066+/qnfv3vr777/l5+fnyCECUM4Qh/NHHHYt4nAFUpIZMMqfwl6Bbd26tVn+6quvtnnP+gqsh4eHIcm47bbbjLNnz9qUy8jIMFJTUw3DMIyEhASjWbNm5nrXXXedcejQoRzbjY2NNR566CGzXM2aNfO8QjV69GiznJeXlzFz5swcZTZs2GDUrVvXLJNd3pErsLGxsUbjxo3NcqGhocZ///vfXK9yJiQkGF9//bUxatSoXOuS1dU4ez3//PPmOjVq1DB++OGHXLc9f/58o1KlSmbZb7/9Ntf6Vq5caVgsFrPcbbfdZly4cMGmTExMjHHHHXfkOI6uugIbGRlpPProo8aGDRuMjIyMXMvExsYa48ePN+tq0qRJnmWtr8B6enqaV24XLVqUo+zChQuNoKAgs/ygQYPy3JclS5YYbm5u5nF67bXXjMTExBzltm3bZjRv3tys86GHHsq1vop4BRYor4jDxGHi8GXE4dKLJBROVZjg9+uvv9qclJ955hmb962DnyRj4MCBeZ5ksr300ktm+aFDhxZY3vqE+Nprr+V4f//+/TYn7Dlz5uRZ1/79+w0/Pz+bNjsS/J577jmzTKVKlYx9+/bluy/5KWzwi4iIMLvqhISE5PoFwtqyZcvM+ps1a5ZrkOzWrZtZpl+/fkZ6enqudWVkZBgDBw60abOrgl9hPPjgg2Z9v//+e65lrIOfJMPNzc34+++/86xz8eLFNuWXLl2ao0xGRobNl6Iff/wx33aePn3aqF69uhmAjx8/nqNMRQx+QHlFHCYOE4cvIw6XXgxMhBKxYMEC3XPPPeayt7e3Hn744XzXee+99/LtZpOWlqYZM2aY9X3yyScFdsuZNm2aOWCA9VD12WbOnGl2s+jUqZNGjBiRZ11NmjRxWteVS5cu2XTdee2113TVVVc5pW57TJ8+XRkZGZKkF154QQ0bNsy3fJ8+fTRo0CBJWd1Ytm3bZvP+3r17tXbtWnP5/fffl7u7e651ubm56YMPPsgxkENpM2rUKPO1vSNK3n333fl2Gerfv79uueUWc/nzzz/PUeaXX34xJ5m/+eabNXTo0Hy3WaNGDfNzmZaWpvnz59vVVgDlG3E4f8Rh4rBEHC5OPBOKYvP777/r/PnzNr+LiYnRxo0bzX/ebO+8806+/e5bt26tZs2a5bu9zZs36+zZs5Kkfv36qVq1agW2MSwsTE2bNtXevXu1e/duxcbG2jwTs3z5cvP1vffeW2B9I0aM0LRp0wosV5D169crJiZGkhQYGJhv0C0Ov//+u/n6rrvusmudvn37atGiRZKyRhBs3769+Z71cezQoYOaN2+eb11NmjRRly5dtG7dusI026nS0tK0YcMG7dixQ1FRUYqPj1d6err5fnx8vPk6r1Ehr3TfffcVWGbEiBH68ccfJdket2xF/dtkW7NmjZ544gm71gNQthGHi444TByWiMPFiSQUxWbTpk3atGlTvmUCAwM1ffp0m6tZuenQoUOB27M+UZ44cUKPPPKIXe3MDjKGYejEiRNm8DMMQzt27DDLde3atcC6mjRpopCQEIeHN7ce5KFLly7y9fV1qL7CiI6ONod69/Ly0osvvmjXetkP9EtZQ8lbs74ia89xzC5XEsEvOTlZ06ZN0yeffJLjy1te7ClnsVjUuXPnAstZH58zZ87o9OnTqlmzpvk762Pyww8/aOXKlQXWGRsba76+8m8DoPwiDhcdcZg4LBGHixNJKFwqICBAVapUUevWrdW/f3/dd999qly5coHrVa1atcAyp06dMl/v3LlTO3fuLHT7rEcJjI2NVWpqqrkcHh5uVx3h4eEOB78zZ86Yrxs0aOBQXYVlPQJgamqqTXcke1kfR0k6d+6c+bowx9HVLl68qL59+9p9RTWb9dXYvAQHByswMLDAclWrVpWPj485wuK5c+dsgp/15/zbb78tVDulnH8bABULcdg+xGHiMHG4eJGEothMnjzZnNzaUfZcgbS+ylRU1t08rpwE2d4htf39/R1uh/XJNCAgwOH6CsPZx1GyPZauPI6FNWbMGDPweXl56b777tMNN9ygZs2aqWbNmvL19TWfoYmMjFT9+vUlZU2gXpDCDMnu7+9vBr8rA6ujf58r/zYAyi/icNERh4nDxOHiRRKKcsP6ZDl27FhNnz7dofquDDpJSUl2nZATExMd2q4kmyt1Vwbh4ma9j0FBQU4JhtbHMikpya51nHEcC+PkyZP65ptvJGUNyvDnn3/mOWedZN9VV2v27rdku+9XXrX19/c3/yZbt25Vu3btCtUOACguxGHnIA4ThysCRsdFuVG9enXzdVRUlMP1VapUSV5eXubysWPH7FrPGX39rfclIiLC4fqKuu24uLhCnbTzYt2Ny5XHsTCWLVtmjsB43XXX5Rv4JOno0aOFqv/ixYt2fZE5f/68zWTnoaGhNu87+3MOAM5CHHYO4jBxuCIgCUW5Yf2w+dq1a80TWVFZLBa1adPGXLYepCAvBw8eVHR0tEPblbIGQci2bt06JScnO1ynvWrWrGkzQqL1kO5FZX2V0J7jKMnlgyFYP+PRqlWrAsuvWrWqUPUbhqENGzYUWM56v6tXr66wsDCb960/53///Xeh2gAAxYk47BzEYeJwRUASinKje/fu5uAKJ06c0C+//OJwndZX4b766qsCy3/55ZcOb1PKCn7BwcGSsrqbOFqvj4+P+TotLa3A8kOGDDFff/TRRw5tW7I9jps3b9a+ffvyLX/o0CGXBz/ruewKuuqclJRUpL/Jf/7znwLLWNeb21Vg67/NrFmzbK7WAkBJIg7njThcMOJwxUISinLD29vbZpLqhx9+WCdPnrR7feuR8LL93//9n/l6/fr1+QbAQ4cO6d1337V7e/m5ctLwp59+Wvv37y9yfVWqVDFf23NMxo8fbz74/9NPP2nOnDl2byu3rinNmjWzmRz6sccey3MQgczMTI0dO9bhK+iFZT364e+//25OEp6b8ePH5/p5KchXX32V71XY5cuX64cffjCX77///hxlbr31VjVq1EhS1giKDz/8sN3HKiEhweXP+ACoOIjDeSMOF4w4XLGQhKJcGT9+vFq0aCEp6yTfsWNHfffdd3meaM+fP6/PPvtM7du315tvvpnj/SZNmmjkyJHm8v3336+5c+fmKLd582YNGDBAiYmJNs+vOOKpp55Sw4YNJWWNxNajRw998803uZ7okpKSNG/ePI0ePTrXulq2bGm+/u677wrcdsOGDTVp0iRzefTo0ZowYUKe83Clp6frr7/+0r333pvnA/qvvPKKLBaLJOmvv/7SXXfdZc4Nly0uLk733nuv/vjjD6cdR3v17dvXHDnv0KFDGjFiRK7te+CBB/TJJ58UetRAT09PZWRkaMiQIVqyZEmO93/77TcNHTrU/PsOGDBA/fr1y1HO3d1dH3/8sfnlZPbs2br++uu1d+/ePLe9fft2Pf3006pTp47Ln20CULEQh4nDRUUcrlgYHRflSkBAgBYuXKj+/fsrIiJCUVFRGjZsmEJDQ9WlSxfVqFFDhmHowoUL2rNnjw4ePGgGxr59++Za5zvvvKN169Zp//79unTpkkaOHKmXXnpJXbt2lbe3t/755x9t3LhRhmHolltuUXR0tF0TFxckKChIP/74owYMGKCzZ8/q/PnzuvPOOzVu3Dh169ZNVatWVUpKig4fPqytW7cqOTnZ5tkZa7feeqsWLVokKetq7h9//KEWLVrI29vbLPPcc8+ZXY+krKH9IyMjNXfuXBmGobffflsffPCBOnbsqIYNG8rPz09xcXGKjIzUzp07zSt71ld7rfXu3VsTJkwwv2R8++23+vXXX9W3b1/VqFFDZ86c0bJly5SQkKDg4GA99thjTptawB7BwcGaMGGCXnrpJUnS119/rT/++EOdO3dWrVq1dPr0aa1YsUKJiYny8PDQRx99pBEjRthdf1hYmIYOHar33ntPAwYMUJs2bdS2bVsZhqEtW7bon3/+McvWrFlTn3/+eZ519e/fXx9//LEeeughZWRk6I8//tCff/6p5s2bq3Xr1goKClJSUpJOnz6tHTt22MwPBwDFiThMHC4q4nAFYwBO1KtXL0OSIcmYPHmyQ3VNnjy5yHVFR0cbt99+u2GxWMw68vupXLmyMWfOnDzrO3nypNGxY8d867jxxhuNuLg4m2OwfPnyXOsbMWKEWWb27Nn57ktkZKTRs2dPu/aje/fuudaRmppaYB0RERG5rvv+++8bwcHBdm3fYrEYN954Y77788wzzxhubm551hEWFmasXbvWmD17tvm7ESNG5FunPew55unp6cZ9991X4Gflp59+MiIiIszf1a1bN9f6riyTmppq/N///V++9V911VXG3r177dqnZcuWGY0bN7brbyPJaNGihXHy5Mkc9Sxfvtws06tXLzuPKIDSiDhMHCYOX0YcLr24E4pyKSQkRPPnz9fu3bs1b948rVixQhEREYqOjpabm5sqV66sRo0aqX379urfv78GDBhgM2jAlcLCwrR+/Xp9+eWX+vrrr7Vz507FxsaqevXqatOmjUaMGKFbb73V7ObiTHXr1tXKlSu1dOlSfffdd1q9erVOnz6tuLg4+fv7q27duurQoYOuv/563XjjjbnW4enpqSVLlmjmzJn64YcftHv3bl24cEGpqakFbv/RRx/VyJEj9Z///EeLFy82r+ilpKQoMDBQtWvXVosWLdS7d28NHjzYZkS/3Lz66qu67bbb9NFHH2nZsmU6ffq0AgICVK9ePd1yyy164IEHFBoa6tCzN0Xl7u6uuXPn6vbbb9dnn32mDRs26OLFiwoODlZ4eLhuuukmjR49WmFhYYqMjCx0/Z6envriiy90++23a+bMmdq0aZNOnz4tf39/NWvWTMOHD9cDDzxgc2U8P3369NHevXu1YMEC/fbbb1q/fr2ioqIUFxcnPz8/Va9eXU2bNlW3bt103XXXqW3btoVuMwAUBXHYFnHYPsThisNiGC5+6hgAAAAAUGExMBEAAAAAwGVIQgEAAAAALkMSCgAAAABwGZJQAAAAAIDLkIQCAAAAAFyGJBQAAAAA4DIkoQAAAAAAlyEJBQAAAAC4DEkoAAAAAMBlSEIBAAAAAC5DEgoAAAAAcBmSUAAAAACAy5CEAgAAAABchiQUAAAAAOAyJKEAAAAAAJf5fzm4L9F2WtgQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(10, 5))\n",
    "\n",
    "plot_confusion_matrix(eec, X_test, y_test, ax=axs[0], colorbar=False)\n",
    "axs[0].set_title(\"Easy Ensemble\")\n",
    "plot_confusion_matrix(rusboost, X_test, y_test, ax=axs[1], colorbar=False)\n",
    "axs[1].set_title(\"RUSBoost classifier\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
